Results for “Problem solving” — Page 9

DOE OSTI2016

MO-F-204-00: Preparing for the ABR Diagnostic and Nuclear Medical Physics Exams

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance of all aspects of clinical medical physics. All parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those unique aspects of the nuclear exam, and how preparing for a second specialty differs from the first. Medical physicists who recently completed each ABR exam portion will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

MO-F-204-01: Preparing for Part 1 of the ABR Diagnostic Physics Exam

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance of all aspects of clinical medical physics. All parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those unique aspects of the nuclear exam, and how preparing for a second specialty differs from the first. Medical physicists who recently completed each ABR exam portion will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

MO-F-204-02: Preparing for Part 2 of the ABR Diagnostic Physics Exam

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance of all aspects of clinical medical physics. All parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those unique aspects of the nuclear exam, and how preparing for a second specialty differs from the first. Medical physicists who recently completed each ABR exam portion will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

MO-F-204-03: Preparing for Part 3 of the ABR Diagnostic Physics Exam

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance of all aspects of clinical medical physics. All parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those unique aspects of the nuclear exam, and how preparing for a second specialty differs from the first. Medical physicists who recently completed each ABR exam portion will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

MO-F-204-04: Preparing for Parts 2 & 3 of the ABR Nuclear Medicine Physics Exam

Adequate, efficient preparation for the ABR Diagnostic and Nuclear Medical Physics exams is key to successfully obtain ABR certification. Each part of the ABR exam presents its own challenges: Part I: Determine the scope of basic medical physics study material, efficiently review this material, and solve related written questions/problems. Part II: Understand imaging principles, modalities, and systems, including image acquisition, processing, and display. Understand the relationship between imaging techniques, image quality, patient dose and safety, and solve related written questions/problems. Part III: Gain crucial, practical, clinical medical physics experience. Effectively communicate and explain the practice, performance, and significance of all aspects of clinical medical physics. All parts of the ABR exam require specific skill sets and preparation: mastery of basic physics and imaging principles; written problem solving often involving rapid calculation; responding clearly and succinctly to oral questions about the practice, methods, and significance of clinical medical physics. This symposium focuses on the preparation necessary for each part of the ABR exam. Although there is some overlap, the nuclear exam covers a different body of knowledge than the diagnostic exam. A separate speaker will address those unique aspects of the nuclear exam, and how preparing for a second specialty differs from the first. Medical physicists who recently completed each ABR exam portion will share their experiences, insights, and preparation methods to help attendees best prepare for the challenges of each part of the ABR exam. In accordance with ABR exam security policy, no recalls or exam questions will be discussed. Learning Objectives: How to prepare for Part 1 of the ABR exam by determining the scope of basic medical physics study material and related problem solving/calculations How to prepare for Part 2 of the ABR exam by understanding diagnostic and/or nuclear imaging physics, systems, dosimetry, safety and related problem solving/calculations How to prepare for Part 3 of the ABR exam by effectively communicating the practice, methods, and significance of clinical diagnostic and/or nuclear medical physics.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

The expressivity of classical and quantum neural networks on entanglement entropy

Abstract Analytically continuing the von Neumann entropy from Rényi entropies is a challenging task in quantum field theory. While then-th Rényi entropy can be computed using the replica method in the path integral representation of quantum field theory, the analytic continuation can only be achieved for some simple systems on a case-by-case basis. In this work, we propose a general framework to tackle this problem using classical and quantum neural networks with supervised learning. We begin by studying several examples with known von Neumann entropy, where the input data is generated by representing$${\text {Tr}}\rho _A^n$$ Tr ρ A n with a generating function. We adopt KerasTuner to determine the optimal network architecture and hyperparameters with limited data. In addition, we frame a similar problem in terms of quantum machine learning models, where the expressivity of the quantum models for the entanglement entropy as a partial Fourier series is established. Our proposed methods can accurately predict the von Neumann and Rényi entropies numerically, highlighting the potential of deep learning techniques for solving problems in quantum information theory.

Physics↗

DOE OSTI2023

Unlocking Metamaterials At The Macro Scale (CRADA Final Report)

This project was part of the Cyclotron Road program, which supports scientific entrepreneurs in their efforts to commercialize novel technologies with potential to address energy, manufacturing and climate related issues. The participants' technology is a lightweight cellular material system which could be applied to a range of products and markets and offer benefits of reduced weight, cost, waste, and carbon footprint. The objective of the project was to investigate potential opportunities and de-risk technical and market barriers in pursuit of successful commercialization. This project’s purpose was to find market pathways and technical roadmaps for commercializing the lightweight cellular material technology. The main problems to overcome are the risks in both tech and market. On the technical side, performance, weight, cost and speed of manufacturing, and other scaleup problems needed to be addressed. On the market side, the challenges included finding product/market fit, developing business models and go to market strategies, and developing commercial relationships within various industries. Our approach typically started with market analysis, in order to identify potential applications where our technology could solve problems and address pain points. To do this, we performed customer discovery, interviewing hundreds of industry stakeholders along all parts of the value chain for a given product or industry. From here, we would develop technoeconomic models which combined aspects of numerical modeling for structural and mechanical performance based on criteria from customers or industry guidelines such as stiffness, strength, weight, and other physical properties. Then, this would be combined with detailed cost models to translate the engineering solution into a manufacturable, scalable product. The challenge here was to have equal or better performance at lower cost and higher speed than existing solutions.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Exact enforcement of temporal continuity in sequential physics-informed neural networks

The use of deep learning methods in scientific computing represents a potential paradigm shift in engineering problem solving. One of the most prominent developments is Physics-Informed Neural Networks (PINNs), in which neural networks are trained to satisfy partial differential equations (PDEs). While this method shows promise, the standard version has been shown to struggle in accurately predicting the dynamic behavior of time-dependent problems. To address this challenge, methods have been proposed that decompose the time domain into multiple segments, employing a distinct neural network in each segment and directly incorporating continuity between them in the loss function of the minimization problem. In this work we introduce a method to exactly enforce continuity between successive time segments via a solution ansatz. This hard constrained sequential PINN (HCS-PINN) method is simple to implement and eliminates the need for any loss terms associated with temporal continuity. The method is tested for a number of benchmark problems involving both linear and non-linear PDEs. Examples include various first order time dependent problems in which traditional PINNs struggle, namely advection, Allen–Cahn, and Korteweg–de Vries equations. Furthermore, second and third order time-dependent problems are demonstrated via wave and Jerky dynamics examples, respectively. Notably, the Jerky dynamics problem is chaotic, making the problem especially sensitive to temporal accuracy. Finally, the numerical experiments conducted with the proposed method demonstrated superior convergence and accuracy over both traditional PINNs and the soft-constrained counterparts.

42 ENGINEERING↗

DOE OSTI2020

Understanding Structures of Cyber Competition in an Era of Major Power Rivalry

Over the past two decades, the cyber domain has emerged and evolved into a key strategic domain for nations across the globe. Security strategies are espoused by heads of state that focus on how to manage the increasingly enormous, crosscutting impact that the cyber domain has on national security across economic, military, intelligence, intellectual property, and countless other facets. These strategies frequently evolve, and even change entirely, as leaders adapt to new technologies and administrations change. Even if these strategies did not change at all, they would take inordinate amounts of time to effectively implement within the organizational structures of government. The time required to go from setting department and agency-level goals, to the time small teams have well-oiled processes and expertise to accomplish tactical objectives that meet the strategic vision is lengthy. With near certainty, by the time objectives and vision are implemented, the landscape, strategy, or both has changed entirely. While this churn will likely never cease due to the rapidly changing nature of the cyber domain, this problem raises an important question: can governmental structures be organized to rapidly adapt to changing cyber strategies? As offices responsible for particular missions in cyberspace shuffle about within the bureaucracy, are technical capabilities enabled or enhanced? No matter how advanced a particular technical capability is or how adept the staff is at solving problems, they will be ineffective if placed haphazardly within the organization: the right authorities may not exist for their office, the correct lines of interpersonal communication may not be established properly, or insufficient resources have not been allocated to effectively deploy a brilliant technical solution. This concept of organizational agility in the context of national cyber capabilities is important when taking into account the National Defense Strategy’s emphasis on cyber capability and the ability of the United States to compete and rapidly adapt to new challenges posed by rivals. As a nation, we are at a point where technology evolves rapidly enough to warrant thoughtful and nimble changes to the bureaucratic structures that support how cyber operations are carried out. Taking these questions and cross-comparing them to the organizational structures across China, Russia, and the United States provides for an interesting thought experiment. As non-democratic regimes, China and Russia have differing priorities and internal power dynamics than the United States and thus organize their governments differently. By combining known and broadcasted strategies of these nations with the observed technical capabilities demonstrated in the public domain, we can begin to see how organizational structures map to strategic goals and directly enable technical capabilities. Insight can be gained by introducing organizational structures into traditional analysis focusing solely around strategies and capabilities; additionally, otherwise unknown capabilities or intents might be discovered or inferred by analyzing organizational structures alone. Analyzing cyber operations from this oft-overlooked perspective could potentially provide useful insight and more concrete actions that can be undertaken to realize the National Defense Strategy’s goals.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2017

A compatible high-order meshless method for the Stokes equations with applications to suspension flows

A stable numerical solution of the steady Stokes problem requires compatibility between the choice of velocity and pressure approximation that has traditionally proven problematic for meshless methods. Here in this work, we present a discretization that couples a staggered scheme for pressure approximation with a divergence-free velocity reconstruction to obtain an adaptive, high-order, finite difference-like discretization that can be efficiently solved with conventional algebraic multigrid techniques. We use analytic benchmarks to demonstrate equal-order convergence for both velocity and pressure when solving problems with curvilinear geometries. In order to study problems in dense suspensions, we couple the solution for the flow to the equations of motion for freely suspended particles in an implicit monolithic scheme. The combination of high-order accuracy with fully-implicit schemes allows the accurate resolution of stiff lubrication forces directly from the solution of the Stokes problem without the need to introduce sub-grid lubrication models.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2018

Stochastic Programming Approach for Resource Selection under Demand Uncertainty

Cost-efficient selection and scheduling of a subset of geographically distributed resources to meet the demands of a scientific workflow is a challenging problem. The problem is exacerbated by uncertainties in demand and availability of resources. In this paper, we present a stochastic optimization based framework for robust decision making in the selection of distributed resources over a planning horizon under demand uncertainty. We present a novel two-stage stochastic programming model for resource selection, and implement an L-shaped decomposition algorithm to solve this model. A Sample Average Approximation algorithm is integrated to enable stochastic optimization to solve problems with a large number of scenarios. Using the metric of stochastic solution, we demonstrate up to 30% cost reduction relative to solutions without explicit consideration of demand uncertainty for a 24-month problem. We also demonstrate up to 54% cost reduction relative to a previously developed solution for a 36-month problem. We further argue that the composition of resources selected is superior to solutions computed without explicit consideration of uncertainties. Given the importance of resource selection and scheduling of complex scientific workflows, especially in the context of commercial cloud computing, we believe that our novel stochastic programming framework will benefit many researchers as well as users of distributed computing resources.

scientific workflows, task scheduling, optimizatio↗

DOE OSTI2022

Multi-angle quantum approximate optimization algorithm

The quantum approximate optimization algorithm (QAOA) generates an approximate solution to combinatorial optimization problems using a variational ansatz circuit defined by parameterized layers of quantum evolution. In theory, the approximation improves with increasing ansatz depth but gate noise and circuit complexity undermine performance in practice. Here, we investigate a multi-angle ansatz for QAOA that reduces circuit depth and improves the approximation ratio by increasing the number of classical parameters. Even though the number of parameters increases, our results indicate that good parameters can be found in polynomial time for a test dataset we consider. This new ansatz gives a 33% increase in the approximation ratio for an infinite family of MaxCut instances over QAOA. The optimal performance is lower bounded by the conventional ansatz, and we present empirical results for graphs on eight vertices that one layer of the multi-angle anstaz is comparable to three layers of the traditional ansatz on MaxCut problems. Similarly, multi-angle QAOA yields a higher approximation ratio than QAOA at the same depth on a collection of MaxCut instances on fifty and one-hundred vertex graphs. Many of the optimized parameters are found to be zero, so their associated gates can be removed from the circuit, further decreasing the circuit depth. These results indicate that multi-angle QAOA requires shallower circuits to solve problems than QAOA, making it more viable for near-term intermediate-scale quantum devices.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

SPARTAN (Scalable Probabilistic Application Reconfigurable Tensor Autonomous Network)

The technical founder of Ludwig Computing Inc has been competitively selected for support by Cyclotron Road, a U.S. Department of Energy (DOE) Advanced Manufacturing Office (AMO) Lab-Embedded Entrepreneurship Program (LEEP) through an approved merit review process. Ludwig Computing Inc, supported by the U.S. Department of Energy's Advanced Manufacturing Office through the Cyclotron Road program, has investigated the advantages of probabilistic computing for real-world compute-intensive applications. This research adds to the understanding of alternative computing paradigms by exploring a unique hardware-software co-design that integrates quantum computing methods with nature-inspired problem-solving techniques. The project's focus on areas such as combinatorial optimization, graph analytics, and machine learning demonstrates the potential for significant advancements in computational efficiency and performance. By harnessing natural randomness to streamline large circuits into fewer devices, Ludwig's approach enables massive parallelism, potentially offering higher throughput, speed, and energy efficiency compared to conventional hardware solutions. This work benefits the public by paving the way for more efficient computing solutions that could address complex real-world problems while potentially reducing energy consumption in data-intensive industries.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Towards developing multiscale-multiphysics models and their surrogates for digital twins of metal additive manufacturing

Artificial intelligence (AI) embedded within digital models of manufacturing processes can be used to improve process productivity and product quality significantly. The application of such advanced capabilities particularly to highly digitalized processes such as metal additive manufacturing (AM) is likely to make those processes commercially more attractive. AI capabilities will reside within Digital Twins (DTs) which are living virtual replicas of the physical processes. DTs will be empowered to operate autonomously in a diagnostic control capacity to supervise processes and can be interrogated by the practitioner to inform the optimal processing route for any given product. The utility of the information gained from the DTs would depend on the quality of the digital models and, more importantly, their faster-solving surrogates which dwell within DTs for consultation during rapid decision-making. In this article, we point out the exceptional value of DTs in AM and focus on the need to create high-fidelity multiscale-multiphysics models for AM processes to feed the AI capabilities. We identify technical hurdles for their development, including those arising from the multiscale and multiphysics characteristics of the models, the difficulties in linking models of the subprocesses across scales and physics, and the scarcity of experimental data. We discuss the need for creating surrogate models using machine learning approaches for real-time problem-solving. We further identify non-technical barriers, such as the need for standardization and difficulties in collaborating across different types of institutions. We offer potential solutions for all these challenges, after reflecting on and researching discussions held at an international symposium on the subject in 2019. Here, we argue that a collaborative approach can not only help accelerate their development compared with disparate efforts, but also enhance the quality of the models by allowing modular development and linkages that account for interactions between the various sub-processes in AM. A high-level roadmap is suggested for starting such a collaboration.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Adaptive Power Flow Approximations With Second-Order Sensitivity Insights

The power flow equations are fundamental to power system planning, analysis, and control. However, the inherent non-linearity and non-convexity of these equations present formidable obstacles in problem-solving processes. To mitigate these challenges, recent research has proposed adaptive power flow linearizations that aim to achieve accuracy over wide operating ranges. The accuracy of these approximations inherently depends on the curvature of the power flow equations within these ranges, which necessitates considering second-order sensitivities. In this paper, we leverage second-order sensitivities to both analyze and improve power flow approximations. We evaluate the curvature across broad operational ranges and subsequently utilize this information to inform the computation of various sample-based power flow approximation techniques. Additionally, we leverage second-order sensitivities to guide the development of rational approximations that yield linear constraints in optimization problems. In conclusion, this approach is extended to enhance accuracy beyond the limitations of linear functions across varied operational scenarios.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Linearization errors in discrete goal-oriented error estimation

This paper is concerned with goal-oriented a posteriori error estimation for nonlinear functionals in the context of nonlinear variational problems solved with continuous Galerkin finite element discretizations. A two-level, or discrete, adjoint-based approach for error estimation is considered. The traditional method to derive an error estimate in this context requires linearizing both the nonlinear variational form and the nonlinear functional of interest which introduces linearization errors into the error estimate. In this paper, we investigate these linearization errors. In particular, we develop a novel discrete goal-oriented error estimate that accounts for traditionally neglected nonlinear terms at the expense of greater computational cost. We demonstrate how this error estimate can be used to drive mesh adaptivity. Here, we show that accounting for linearization errors in the error estimate can improve its effectivity for several nonlinear model problems and quantities of interest. We also demonstrate that an adaptive strategy based on the newly proposed estimate can lead to more accurate approximations of the nonlinear functional with fewer degrees of freedom when compared to uniform refinement and traditional adjoint-based approaches.

42 ENGINEERING↗

DOE OSTI2020

2020 Exascale Computing Project Annual Meeting (Executive Summary Report)

The Exascale Computing Project (ECP) delivers specific applications, software products, and outcomes on DOE computing facilities. Integration across these elements for specific hardware technologies for exascale system instantiations is fundamental to ECP success. The outcome of the ECP is the delivery of a capable exascale computing ecosystem to provide breakthrough solutions addressing our most critical challenges in scientific discovery, energy assurance, economic competitiveness, and national security. This outcome is not a matter of ensuring more powerful computing systems. The ECP is designed to create more valuable and rapid insights from a wide variety of applications (“capable”), which requires a much higher level of inherent efficacy in all methods, software tools, and ECP-enabled computing technologies to be acquired by DOE laboratories (“ecosystem”). The ECP annual meeting provides a unique opportunity for the core technical expertise in the United States focused on achieving this next plateau of computational science and computing performance to engage in direct discussions on project execution. Face-to-face gatherings in technical communities like this are common and needed for the exchange of scientific ideas and technical performance. The ECP annual meeting stands apart from other technical conferences and meetings in the computing community as it is uniquely and solely focused on the execution of the ECP and the integration of technical activities leading to the creation of the exascale computing ecosystem for the future. The direct interaction of key critical technical staff, who are leaders in their respective fields, and the resulting give-and-take between software, applications, and hardware and the technical co-design therein, is unique and essential to the effective execution of the ECP. The first annual meeting was held in Knoxville, Tennessee, January 31 – February 2, 2017 and brought together, for the first time, a diverse collection of researchers from 16 DOE national laboratories as well as university computer and computational science researchers to discuss shared problems and joint solutions for the development of a capable exascale computing ecosystem. These interactions resulted in focused technical plans and an energized community centered on advances for ECP. The second annual meeting was held in Knoxville, Tennessee, February 5–9, 2018. It included 643 individual thought leaders and performers in application development, software research and deployment, and hardware research and integrators, all of whom are part of the multifaceted, billion dollar HPC community. This meeting provided a platform to discuss and disseminate numerous examples where researchers with common goals and synergistic solutions came together for the first time to deliver tangible results. Additionally, at the 2018 meeting, ECP researchers had the opportunity to digest all US HPC vendor R&D product roadmaps pointing to exascale – not only to learn how their research can play a role, but, more importantly, to influence those roadmaps to ensure successful delivery on DOE applications that will contribute to (if not solve) problems of national interest in national security, science, energy, and health, as well as growing security threats. The third annual meeting was held in Houston, Texas, January 14–17, 2019. With a 19% increase in the number of registrations (768 people), and the change in location, the third annual meeting was considered the most impactful of the three at the time. The new website provided a better platform for the dissemination of the content, the new venue as a meeting hotel instead of a conference center facilitated interactions and discussions after event hours, and the addition of an award-winning mobile event conference app (Whova) transformed dramatically the attendee experience at the event. This fourth annual meeting was held in Houston, Texas, February 3-7, 2020. This meeting had an increase in the number of attendees for a total of 824 people registered (782 attendees) and included numerous enhancements based on feedback and lessons learned from previous meetings, some of which are listed here: Improved quality of the sessions, their material and the whole program.; Had more industry participation and addition of external collaborators from overseas.; Published the full agenda earlier to better accommodate attendance and travel plans based on schedule.; Centralized all sessions in one venue.; Provided additional hotels and room blocks for the attendees.; Improved communication with the audience (links, material, directions, notifications, etc.) to go paperless.; Enhanced side meeting scheduling, management and user experience.; Made available additional space and tables for impromptu meetings and side discussions.; Improved IT and A/V solutions for speakers. In addition, our final survey captured the following points as opportunities for improvement in future meetings: consider a different meeting location that is more pedestrian friendly, reduce talks during working meals to allow more collaboration and informal time, adapt the agenda to acknowledge attendees from different timezones, consider recording some of the tutorials and/or sessions to share broadly with the HPC community, have a larger poster room, provide additional power strips, and improve the WiFi.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Smoothing by cubic spline modified applied to solve inverse thermal problem

This paper presents an alternative to cubic spline regularization and its weighted form applied in solving inverse thermal problems. The inverse heat transfer problems are classified as ill-posed, that is, the solution may become unstable, mainly because they are sensitive to random errors deriving from the input data, necessitating a regularization method to soften these effects. The smoothing technique proposed by cubic spline regularization ensures that the global data tend to be more stable, with fewer data oscillations and dependent on a single arbitrary parameter input. It also shows that the weighted cubic spline is able to enhance filter action. The methods have been implemented in order for the search engine to optimize the choice of parameters and weight and, thus, the smoothing gains more flexibility and accuracy. The simulated and experimental tests confirm that the techniques are effective in reducing the amplified noise by inverse thermal problem presented.

97 MATHEMATICS AND COMPUTING↗