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DOE OSTI2025

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty [Brochure]

The four priority research directions outlined in this brochure represent a cohesive vision for advancing the science of inverse problems for complex systems under uncertainty. Together, they address the critical challenges of: discovering, exploiting, and preserving physical and problem structure; overcoming model limitations; integrating disparate, multimodal, and/or dynamic data; and tailoring the solution of inverse problems to downstream tasks. While each PRD focuses on a distinct aspect of inverse-problem research, their interconnected nature highlights the importance of a holistic approach that leverages progress across all areas to achieve transformative solutions. This agenda calls for research across mathematics, statistics, and computer science disciplines, which are guided and complemented by rapid advances in artificial intelligence, high-performance computing, and experimental facilities, to unlock new capabilities, maximize scientific impact, and meet the growing demands of inverse problems that arise across applications that are critical to DOE's mission.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

ARM Aerial Instrument Workshop Report

The mission of the U.S. Department of Energy’s (DOE) Biological and Environmental Research (BER) program is to “support transformative science and scientific user facilities to achieve a predictive understanding of complex biological, earth, and environmental systems for energy and infrastructure security, independence, and prosperity.” (https://science.osti.gov/ber) Aligned with the BER central mission, the Earth and Environmental Systems Sciences Division (EESSD) plays a vital role in supporting the fundamental research to understand and predict Earth’s climate and environmental systems, and is also in a unique position to inform the development of sustainable solutions to the nation’s energy and environmental challenges. Specifically, EESSD manages two scientific user facilities: the Atmospheric Radiation Measurement (ARM) user facility and the Environmental Molecular Sciences Laboratory (EMSL). These facilities provide the broader scientific community with scientific expertise, technical capabilities, and unique data sets to facilitate science in areas of importance to DOE. As a multi-platform scientific user facility, ARM aims to fulfill the needs predominantly within the EESSD Atmospheric System Research (ASR) and the Earth and Environmental System Modeling (EESM) mission areas, and provide the critical measurements required to improve understanding of aerosol and cloud life cycles and their interactions, and their coupling with the Earth’s surface. Over the years, ARM has carried out piloted and unmanned aircraft campaigns under different organizational and operational paradigms (Schmid et al. 2014, 2016). Building on its success, the ARM Aerial Facility (AAF) continues to complement the ground-based observations with airborne in situ cloud, aerosol, and trace gas observations as well as measurements of atmospheric state and atmospheric radiation. During the past three years, ARM has managed field campaigns using unmanned aerial systems (UAS) and tethered balloon systems (TBS) at Oliktok Point in Alaska to improve understanding of atmospheric processes in the Arctic. In 2019, following a careful evaluation of scientific community needs, ARM acquired a Bombardier Challenger 850 regional jet to replace the vintage Grumman Gulfstream-159 turboprop aircraft previously used by AAF. With this new “laboratory in the sky”, AAF is evaluating its current and future aerial observation capabilities to continue satisfying the needs of the research community.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

ARM Cloud and Precipitation Measurements and Science Group (CPMSG) 2024 Workshop Report

The mission of the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility is to improve the understanding and representation of cloud and aerosol processes and their interaction with the Earth's surface in Earth system models (ESMs) by providing comprehensive field observations and supporting advanced data analytics. The ARM Cloud and Precipitation Measurements and Science Group (CPMSG) was chartered in March 2019 to help improve the performance and scientific impact of ARM measurements of clouds and precipitation. The group aims to identify and address gaps in measurement capabilities, maximize the scientific impact of ARM data, and effectively serve the scientific community. To achieve these goals, the group includes experts in cloud and precipitation science, as well as representatives from ARM infrastructure, including instrument mentors, engineers, data quality officers, and data product translators. Prior to CPMSG, early discussions on cloud and precipitation measurements primarily focused on improving radar systems, but have since evolved to include a broader scope involving radiometers and other instruments. Since its formation, the CPMSG has gathered feedback using science traceability matrices. CPMSG aims to keep these as living documents to show the measurement needs, scientific drivers, roadblocks, maturity of measurements and retrievals, and pathways to model improvements. The group meets quarterly to discuss and prioritize measurement and operational improvements.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

ARM Cloud and Precipitation Measurements and Science Group (CPMSG) 2024 Workshop Report

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

ARM Cloud and Precipitation Measurements and Science Group (CPMSG) 2024 Workshop Report

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Deployable sensor system using mesh networking and satellite communication

A sensor system may be configured for continuous operation in a low resource environment and/or in extreme environmental conditions. The sensor system may have sufficient processing capabilities to provide scientific computing for pre-processing, quality control, statistical analysis, event classification, data compression and corrections (e.g., spikes in the data), autonomous decisions and actions, triggering other nodes, and information assurance functions that provide data confidentiality, data integrity, authentication, and non-repudiation. The hardware may have both mesh networking and satellite and cellular communication capability, and may be available for relatively low cost. Such a network provides the flexibility to have potentially any number of nodes be completely independent from one another. Thus, the network may scale across a diverse terrain.

Frigo, Janette↗

DOE OSTI2025

PvaPy streaming framework for real-time data processing

User facility upgrades, new measurement techniques, advances in data analysis algorithms as well as advances in detector capabilities result in an increasing amount of data collected at X-ray beamlines. Some of these data must be analyzed and reconstructed on demand to help execute experiments dynamically and modify them in real time. In turn, this requires a computing framework for real-time processing capable of moving data quickly from the detector to local or remote computing resources, processing data, and returning results to users. In this paper, we discuss the streaming framework built on top of PvaPy, a Python API for the EPICS pvAccess protocol. We describe the framework architecture and capabilities, and discuss scientific use cases and applications that benefit from streaming workflows implemented on top of this framework. We also illustrate the framework's performance in terms of achievable data-processing rates for various detector image sizes.

EPICS pvAccess↗

DOE OSTI2024

Portable, heterogeneous ensemble workflows at scale using libEnsemble

libEnsemble is a Python-based toolkit for running dynamic ensembles, developed as part of the DOE Exascale Computing Project. The toolkit utilizes a unique generator–simulator–allocator paradigm, where generators produce input for simulators, simulators evaluate those inputs, and allocators decide whether and when a simulator or generator should be called. The generator steers the ensemble based on simulation results. Generators may, for example, apply methods for numerical optimization, machine learning, or statistical calibration. libEnsemble communicates between a manager and workers. Flexibility is provided through multiple manager–worker communication substrates each of which has different benefits. These include Python’s multiprocessing, mpi4py, and TCP. Multisite ensembles are supported using Balsam or Globus Compute. We overview the unique characteristics of libEnsemble as well as current and potential interoperability with other packages in the workflow ecosystem. We highlight libEnsemble’s dynamic resource features: libEnsemble can detect system resources, such as available nodes, cores, and GPUs, and assign these in a portable way. These features allow users to specify the number of processors and GPUs required for each simulation; and resources will be automatically assigned on a wide range of systems, including Frontier, Aurora, and Perlmutter. Such ensembles can include multiple simulation types, some using GPUs and others using only CPUs, sharing nodes for maximum efficiency. We also describe the benefits of libEnsemble’s generator–simulator coupling, which easily exposes to the user the ability to cancel, and portably kill, running simulations based on models that are updated with intermediate simulation output. We demonstrate libEnsemble’s capabilities, scalability, and scientific impact via a Gaussian process surrogate training problem for the longitudinal density profile at the exit of a plasma accelerator stage. In conclusion, the study uses gpCAM for the surrogate model and employs either Wake-T or WarpX simulations, highlighting efficient use of resources that can easily extend to exascale.

Dynamic ensembles↗

DOE OSTI2020

ECP ST Capability Assessment Report (CAR) for VTK-m (FY20)

The ECP/VTK-m project is providing the core capabilities to perform scientific visualization on Exascale architectures. The ECP/VTK-m project fills the critical feature gap of performing visualization and analysis on processors like graphics-based processors. The results of this project will be delivered in tools like ParaView, Vislt, and Ascent as well as in stand-alone form. Moreover, these projects are depending on this ECP effort to be able to make effective use of ECP architectures. One of the biggest recent changes in high-performance computing is the increasing use of accelerators. Accelerators contain processing cores that independently are inferior to a core in a typical CPU, but these cores are replicated and grouped such that their aggregate execution provides a very high computation rate at a much lower power. Current and future CPU processors also require much more explicit parallelism. Each successive version of the hardware packs more cores into each processor, and technologies like hyper threading and vector operations require even more parallel processing to leverage each core's full potential. VTK-m is a toolkit of scientific visualization algorithms for emerging processor architectures. VTK-m supports the fine-grained concurrency for data analysis and visualization algorithms required to drive extreme scale computing by providing abstract models for data and execution that can be applied to a variety of algorithms across many different processor architectures. The ECP/VTK-m project is building up the VTK-m codebase with the necessary visualization algorithm implementations that run across the varied hardware platforms to be leveraged at the Exascale. We will be working with other ECP projects, such as ALPINE, to integrate the new VTK-m code into production software to enable visualization on our HPC systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Cryogenic User Facilities for R&D on Noble Liquid Detectors and Low Temperature Devices

Cryogenic test facilities are critical infrastructure for physics experiments in a variety of fields, perhaps most notably for particle detection with noble liquid detectors, low-temperature device development, and quantum information research. However, considerable investment and technical knowledge are required to construct and operate such facilities. This white paper discusses proposals for user facilities aimed at broadening the availability of testing capabilities for the scientific community.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2025

OLCF’s Advanced Computing Ecosystem (ACE): FY25 Update for Ongoing Efforts

The advent of widespread use of artificial intelligence (AI) and machine learning (ML) models in science, coupled with fast data production rates of scientific instruments strain the traditional batch-oriented high-performance computing (HPC) environment. As scientific exploration continues to require more data and faster processing and analysis, new emerging technologies and capabilities to enable cross-facility and time-sensitive workflows are required for seamless integration of HPC and experimental facilities. The Advanced Computing Ecosystem (ACE) is a strategic initiative within the Oak Ridge Leadership Computing Facility (OLCF) established in 2024 to support the development of cutting-edge technologies to advance computational research and infrastructure at OLCF and across the Department of Energy (DOE). Several DOE initiatives are spearheading the evolution of the scientific landscape by blurring facility boundaries and connecting the user facilities to advance scientific capabilities and ensure energy dominance. The DOE Integrated Research Infrastructure (IRI) program is one example that is laying a foundation to support complex cross-facility workflows. The IRI program aims to integrate diverse computational resources, data infrastructures, and scientific instruments to facilitate collaboration and accelerate scientific discovery. The Interconnected Science Ecosystem (INTERSECT) initiative at Oak Ridge National Laboratory (ORNL) is another example that aims to revolutionize scientific research through AI-driven, interconnected autonomous laboratories and research facilities. Finally, the American Science Cloud (AmSC), recently announced in the “One Big Beautiful Bill”, aims to leverage prior infrastructure efforts of the IRI and automation and AI efforts of INTERSECT (and others) to build a federated, AI-augmented AmSC platform to unify the DOE’s computing, experimental, and data resources to catalyze scientific innovation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Blueprint for DOE Quantum Supercomputing: Ensuring U.S. Leadership in the Quantum Decade

Quantum computing stands at the threshold of a transformative decade, where the field will evolve from small-scale demonstrations toward practical scientific computing at scale. This Blueprint identifies fault-tolerant quantum computers (FTQCs) as a viable, scalable, and broadly applicable path to achieving “quantum scientific utility,” defined as solving scientifically valuable problems beyond the reach of conventional, classical computers. This capability is expected to show scientific demonstrations in the late 2020s and to mature in the early-to-mid 2030s. This Blueprint outlines a strategy to prepare the U.S. Department of Energy (DOE) for FTQCs and their integration into the U.S. national scientific computing infrastructure. Its purpose is to identify the steps, milestones, and research directions necessary for DOE to enable initial deployment of FTQCs in 2028 as a scientific tool for the nation and mature this capability into the 2030s. DOE has a long history of supporting quantum information science and technology, contributing significantly to research advancements, training a quantum-ready workforce, and providing access to early small-scale quantum hardware. Given recent demonstrations of logical operations on error-corrected logical qubits and the advancement of commercial hardware roadmaps, DOE should begin preparations for large-scale, fault-tolerant quantum computing deployment for DOE science missions. This Blueprint proposes that DOE focus on (1) deploying first-generation scientifically relevant quantum computers with at least 100 logical qubits and performing at least 10,000 to 100,000 hard logical operations in scientifically relevant calculations; (2) developing essential FTQC programming competencies, system software, and facility readiness; and (3) investing in cutting edge focused R&D that fosters breakthroughs in scientific applications, algorithms, and logical architectures needed to accelerate the advent of scientific utility. This effort will position DOE to transition to larger systems: production-scale quantum computers that comprise 1,000 to 10,000 logical qubits, perform 1 to 10 billion hard logical operations, and execute scientifically useful computations at scale. Achieving these goals will require DOE facilities to evolve with urgency to support scientific campaigns that integrate quantum and classical computing resources into efficient workflows, novel software and firmware environments for compiling and routing quantum programs on FTQC machines, and suitable infrastructure for quantum hardware. It will also require further development and optimization of scientific applications from the fields of materials science, quantum chemistry, and high-energy and nuclear physics. The Blueprint calls for transformative R&D and collective action to accelerate the advent of scientific quantum utility and bring it within reach by 2028.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Privacy Preserving Federated Learning for Advanced Scientific Ecosystems

We present a framework to provide privacy preserving (PP) federating learning (FL) across multiple computational and experimental facilities. This work joins the compute capabilities of National Energy Research Scientific Computing Center (NERSC) and Oak Ridge National Laboratory Research Cloud (ORC) with simulated experimental data, such as those produced at the SLAC National Accelerator Laboratory and Spallation Neutron Source (SNS). We describe the software infrastructure developed to provide privacy for computational and experimental networks. We developed algorithmic privacy across the federated system by embedding database security, computation, and communication into the federation architecture, utilizing scientific tools developed by the experimental community.

Archibald, Rick [ORNL] (ORCID:0000000245389780)↗

DOE OSTI2024

Opportunities for retrieval and tool augmented large language models in scientific facilities

Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron facilities are revolutionizing our understanding of materials across the spectrum of the physical sciences, from life sciences to microelectronics. However, these facility and instrument upgrades come with a significant increase in complexity. Driven by more exacting scientific needs, instruments and experiments become more intricate each year. This increased operational complexity makes it ever more challenging for domain scientists to design experiments that effectively leverage the capabilities of and operate on these advanced instruments. Large language models (LLMs) can perform complex information retrieval, assist in knowledge-intensive tasks across applications, and provide guidance on tool usage. Using x-ray light sources, leadership computing, and nanoscience centers as representative examples, we describe preliminary experiments with a Context-Aware Language Model for Science (CALMS) to assist scientists with instrument operations and complex experimentation. With the ability to retrieve relevant information from facility documentation, CALMS can answer simple questions on scientific capabilities and other operational procedures. With the ability to interface with software tools and experimental hardware, CALMS can conversationally operate scientific instruments. By making information more accessible and acting on user needs, LLMs could expand and diversify scientific facilities’ users and accelerate scientific output.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

A2SD: Accelerating Scientific Innovation Through Autonomous Discovery Systems

The 2025 Advancing Autonomous Scientific Discovery (A2SD) workshop convened researchers from academia, national laboratories, and industry to explore the transformative role of autonomy in scientific discovery. The workshop highlighted a convergence of artificial intelligence, robotics, and computational workflows into autonomous systems capable of accelerating the scientific process. Presentations and discussions spanned autonomous experimentation, intelligent workflow orchestration, digital twins, and agent-based systems for managing complex research ecosystems. Key challenges discussed included interoperability across heterogeneous infrastructures, near real-time data management under FAIR principles, reproducibility, and the integration of human oversight. The workshop also emphasized the need for modular software interfaces, federated learning models, and education initiatives to support a next-generation scientific workforce.

Taufer, Michela [University of Tennessee, Knoxvill↗

DOE OSTI2025

Controlled environment agriculture: An opportunity to strengthen interagency research collaboration in the US government

Challenges facing food production and agricultural systems are increasingly interconnected with economic, security, health, and equity issues, among others. Threats such as extreme weather, economic volatility, and shrinking water resources and arable land, influence our ability to maintain a safe and resilient food supply. One promising solution to these threats is controlled environment agriculture (CEA). In many cases, CEA can drastically reduce the amount of water and land used in crop production while increasing productivity. Operations may be established in nearly any environment and harvests can take place year-round, supporting food system resiliency and sustainability. CEA sits at the nexus of a number of disciplines and industries, making it well suited for transdisciplinary and multi-institutional research coordination. Herein, authors from multiple US government agencies present CEA as a case study in improving cross-agency research collaboration. The federal government houses a range of scientific expertise and research capabilities, positioning scientists to lead national and global efforts in transdisciplinary, interagency approaches to complex challenges. Navigating cross-agency collaboration can be a challenge, especially coordinating across different scientific disciplines, geographic locations, and funding mechanisms. To enhance multiagency efforts, collaborators could prioritize (i) organizing personnel and resources, (ii) enhancing existing multiagency collaborations, and (iii) focusing on further opportunities for coordination. Adopting these approaches could enable federal researchers to reinforce and advance academic and industry efforts to address current CEA challenges while solidifying the United States as a leader in this arena.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Position Papers for Inverse Methods for Complex Systems under Uncertainty Workshop

The ability to solve inverse problems – inferring unknown parameters, structures, or states of a system from observed data – is essential for advancing scientific discovery and innovation capabilities for the DOE mission. Basic research needs and challenges are particularly acute in emerging areas such as the interactive, data-driven, modeling and simulation of digital twins; decision support for experiments at DOE scientific user facilities; and for other complex systems and workflows. Inverse problems are at the heart of understanding and controlling complex systems due to factors such as observational data with varying modalities and fidelities, inherent uncertainties in physical measurements and numerical models, and the computational demands of rapid and high-fidelity simulations. The convergence of recent scientific computing trends – scientific machine learning, artificial intelligence, and computing advances such as exascale computing – is creating unprecedented opportunities. These advancements offer the potential to revolutionize how we approach inverse problems to extract actionable insights with the required level of accuracy and computational efficiency. This workshop and the Call for Position Papers are vital steps in bringing together experts to collectively explore and identify the new computational and mathematical directions needed in inverse methods for complex systems under uncertainty.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Looking back at 50 years of cutting-edge capabilities: LANSCE ‘underpins Los Alamos as a world-class scientific institution’

What started with an idea and a memo has since grown into a one-of-a-kind, world-renowned facility that contributes to the nation’s advancement of science and technology. First known as LAMPF (Los Alamos Meson Physics Facility) until the 1990s when it became LANSCE (Los Alamos Neutron Science Center), the facility is celebrating the 50th anniversary of its first full-capacity proton beam on June 9, 1972. Since then, LANSCE has contributed to a range of national security goals, such as supporting the Lab’s stockpile stewardship program, helping to advance the science of medical radioisotopes, improving the understanding of hydrodynamics, and more. “LANSCE has served the nation through its near-unique capabilities,” said Alan Carr, Senior Historian at the National Security Research Center, which houses the facility’s evolution through decades of documents, photos and other materials. “Since 1972, LANSCE has been the Lab’s major experimental science facility and underpins Los Alamos as a world-class scientific institution. This was true 50 years ago and is still true today.” In commemoration of this anniversary, we’re taking a look back at LANSCE’s historic five decades, including its first director, earliest milestones and important contributions to national security.

43 PARTICLE ACCELERATORS↗