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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Fast model-based scenario optimization in NSTX-U enabled by analytic gradient computation

Model-based optimization offers a systematic approach to advanced scenario planning. In this case, the feedforward-control inputs (actuator trajectories) that are needed to attain and sustain a desired scenario are obtained by solving a nonlinear constrained optimization problem. This class of problems generally minimize a cost function that measures the difference between desired and actual plasma states. Several numerical optimization algorithms, such as sequential quadratic programming, require repeated calculation of the cost function gradients with respect to the input trajectories. Calculating these gradients numerically can be computationally intensive, increasing the time needed to solve the feedforward-control optimization problem. Here, this work introduces a method to analytically calculate these cost function gradients from the current profile evolution model. This can significantly reduce the computational time and allow for fast feedforward-control optimization, which would eventually enable optimal scenario planning between discharges. The performance of the feedforward optimizer with analytical gradients is compared to a traditional optimization algorithm based on numerical gradients for different NSTX-U scenarios. The plasma dynamics in the optimization algorithm are simulated using the Control Oriented Transport SIMulator (COTSIM). Results of the work show that analytical gradients consistently reduce the computation time while achieving trajectories that are comparable to those obtained by traditional optimization algorithms based on numerical gradients.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Interpreting the Operando X-ray Absorption Near-Edge Structure of Supported Cu and CuPd Clusters in Conditions of Oxidative Dehydrogenation of Propane: Dynamic Changes in Composition and Size

Supported subnano-cluster catalysts are highly dynamic, developing true active sites only under the pressures and temperatures of reaction conditions. Operando X-ray absorption near-edge structure (XANES) spectroscopy can track changes in the oxidation state and the local environment of cluster atoms, providing insight into the development of these active sites. While bulk metal, oxide, and hydroxide standards are often used for fitting experimental XANES spectra to obtain average oxidation states, we recently showed that computed cluster standards of relevant compositions are a more suitable basis, producing more accurate fits. Here, we theoretically interpret the operando XANES of supported Cu 3 Pd and Cu 4 clusters during temperature-programmed reaction (TPRx) of oxidative dehydrogenation of propane. We use an expanded basis set including both globally optimized computed clusters and bulk standards. Not only can we track reversible composition/oxidation state change with temperature, but also the irreversible growth of the bulk fraction upon heating, which we attribute to cluster sintering. This has important implications for the mechanism of the catalyzed reaction and the nature of the available active sites. Here, we propose that operando XANES provides most significant insight into the nature of supported cluster catalysts in reaction conditions when interpreted using mixed computed cluster and bulk standards.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Deep Learning for Enhancing Fault Tolerant Capabilities of Scientific Workflows

Deep Learning for Enhancing Fault TolerantCapabilities of Scientific WorkflowsAlok Singh, Ilkay AltintasSan Diego Supercomputer Center, UCSDLa Jolla, CA, USA{a1singh, ialtintas}@ucsd.eduMalachi Schram, Nathan TallentPacific Northwestern National LaboratoryRichland, WA, USA{malachi.schram, nathan.tallent}@pnnl.govAbstract-In the history of Computer Science, the act of ‘delegation’ has been the greatest multiplier of society’s problem-solving ability. A scientist working on detecting anomalies in a phenomenon, does not need to re-invent matrix multiplication techniques to solve her problem. Scientific workflows provide ultimate ‘delegation’ mechanism -where a domain scientist can completely forget the specifics of ‘how’ her program will execute on a large cluster in an efficient and cost-effective manner and can instead focus on the mathematical formulation and theoretical robustness of her solution. We present here an approach that directly aims to make the execution of Scientific Workflows more reliable, robust and efficient. We aim that the work presented in this paper will propel the larger effort, from the scientific workflow community, of making scientific workflow execution as simple, efficient and robust as a JOIN operation in a modern database. Specifically, we apply Deep Learning techniques to develop a mechanism that forecasts the final state (success or failure) of a dynamic job in a large-scale particle physics experiment, with minimal data gathering, and as early as possible in job’s life cycle. The key advantage of having a predictive mechanism to identify and anticipate failure-prone jobs is the potential for designing intelligent Fault Tolerance mechanisms to handle anomalous events. We achieve a 14% improvement in computational resources utilization, and an overall classification accuracy of 85% on real tasks executed in a High Energy Physics Computing workflow.To the best of our knowledge, this is the most exhaustive and first of its kind study of neural network architectures in context of a real-dataset profiled from a large-scalescientific workflow.

deep learning↗
DOE OSTI2019

Automatic Differentiation for Adjoint Stencil Loops

Stencil loops are a common motif in computations including convolutional neural networks, structured-mesh solvers for partial differential equations, and image processing. Stencil loops are easy to parallelise, and their fast execution is aided by compilers, libraries, and domain-specific languages. Reverse-mode automatic differentiation, also known as algorithmic differentiation, autodiff, adjoint differentiation, or back-propagation, is sometimes used to obtain gradients of programs that contain stencil loops. Unfortunately, conventional automatic differentiation results in a memory access pattern that is not stencil-like and not easily parallelisable. In this paper we present a novel combination of automatic differentiation and loop transformations that preserves the structure and memory access pattern of stencil loops, while computing fully consistent derivatives. The generated loops can be parallelised and optimised for performance in the same way and using the same tools as the original computation. We have implemented this new technique in the Python tool PerforAD, which we release with this paper along with test cases derived from seismic imaging and computational fluid dynamics applications.

Automatic Differentiation↗
DOE OSTI2026

Universal Injector at LERF – Baseline Design

A new electron injector complex has been proposed within the Low Energy Recirculating Facility (LERF) vault, which would serve both the 22 GeV CEBAF and the positron programs, while being compatible with the electron source required to produce positrons for Ce+BAF. The baseline design of a 3-pass recirculator features a main linac configured with three cryomodules, five isochronous arcs and three straight sections, allowing electron beam acceleration up to 650 MeV. The injector offers the flexibility to extract 1-pass and 2-pass energy electrons, as needed for positron production, with final reconfiguration to a 3-pass 650 MeV injector for 22 GeV CEBAF. This paper provides an overview of the baseline design of the Universal injector complex, including the 8 MeV injector and the recirculator. A comprehensive suite of beam dynamics studies to validate our design is being launched, starting with start-to-end tracking with Elegant followed by the orbit correction and error mitigation.

Bogacz, A. [Thomas Jefferson National Accelerator ↗
DOE OSTI2025

Preliminary modeling of triply periodic minimal surface (TPMS) structures using RELAP5-3D

With the United States Department of Energy (DOE)’s goal of quadrupling the nation’s nuclear energy supply by 2050, and with the Advanced Fuels Campaign pushing for new types of advanced reactor fuels and geometries, the need has arisen for new nuclear fuel designs. One such design is to swap out current nuclear fuel geometries in exchange for another type of geometry, called a Triply Periodic Minimal Surface (TPMS). TPMSs are self-supporting, infinitely repeating lattices—attributes that lend themselves well to additive manufacturing. These surfaces also possess enhanced heat transfer properties thanks to their internal area changes and large surface-area-to-volume ratios. Their drawback, however, is an increased pressure drop. Given the small amount of correlations and data (Reynolds numbers in the 2,000–8,000 range), and the minimal amount of experience so far obtained by modeling TPMS structures using 1D systems codes such as the Reactor Excursion and Leak Analysis Program (RELAP5-3D), further research into this topic was needed. Using data from the University of Wisconsin - Madison (UW), curve fits were created for both a Heat Transfer Coefficient (HTC) correlation and a Darcy friction factor empirical coefficient correlation. The curves’ coefficients and multipliers were then output and utilized in RELAP5-3D models of two upcoming experiments—Flow Loop for INFLUX Pressure drop (FLIP) and Microreactor Agile Non-nuclear Experimental Test (MAGNET)—aimed at increasing the available data for Reynolds numbers to the 16,000–36,000 range for TPMS structures. The models were run under the conditions utilized by a Computational Fluid Dynamics (CFD) analysis performed by another group at Idaho National Laboratory. Only CFD pressure drop values were obtained from the FLIP test, and those values showed that the RELAP5-3D models had a lower rate of pressure increase in comparison to the CFD values. In addition, there seemed to be a vertical shift upward in the pressure drop for both models whenever the TPMS porosity decreased, and the RELAP5-3D models showed a higher vertical shift in comparison to the CFD values. The MAGNET results did not correspond to any CFD or experimental results against which they could be compared, so they were instead compared against the proposed CFD input conditions. These values were then compared with each other to make sure the model seemed to be performing as expected, paving the way for future tests that can be run for the purpose of further analyses and comparisons. The pressure drop increased with temperature and mass flow rate independently. The temperature change would decrease with increasing mass flow rate and temperature, which was just as we expected based on the fact that the lower viscosity and decreased density would result in higher friction and churning losses. The last metric that was assessed was the enthalpy flow change, which increased with increasing mass flow rate and decreasing temperature.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Out of the Dark and Into the Light: A New View of Phytochrome Photobodies

Light is a critical environmental stimulus for plants, serving as an energy source via photosynthesis and a signal for developmental programming. Plants perceive light through various light-responsive proteins, termed photoreceptors. Phytochromes are red-light photoreceptors that are highly conserved across kingdoms. In the model plant Arabidopsis thaliana , phytochrome B serves as a light and thermal sensor, mediating physiological processes such as seedling germination and establishment, hypocotyl growth, chlorophyll biogenesis, and flowering. In response to red light, phytochromes convert to a biologically active form, translocating from the cytoplasm into the nucleus and further compartmentalizes into subnuclear compartments termed photobodies. PhyB photobodies regulate phytochrome-mediated signaling and physiological outputs. However, photobody function, composition, and biogenesis remain undefined since their discovery. Based on photobody cellular dynamics and the properties of internal components, photobodies have been suggested to undergo liquid-liquid phase separation, a process by which some membraneless compartments form. Here, we explore photobodies as environmental sensors, examine the role of their protein constituents, and outline the biophysical perspective that photobodies may be undergoing liquid-liquid phase separation. Understanding the molecular, cellular, and biophysical processes that shape how plants perceive light will help in engineering improved sunlight capture and fitness of important crops.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

An Advanced Extreme Environment Wireless Telemetry System for Turbine Blade Instrumentation

As advanced natural gas power generation systems evolve, the thrust for increased efficiencies and reduced emissions results in increasingly harsh conditions inside the turbine environment. These high temperatures, pressures, and corrosive atmospheres result in accelerated rates of degradation, leading to failure of turbine materials and components. The University of Arkansas (UA) and Siemens, in collaboration with the DoE's National Energy Technology Laboratory (NETL), are developing a reliable and long-term monitoring capability in the turbine hot gas path in the form of novel ceramic-based thermocouples and integrated wide band gap instrumentation electronics that will contribute to the overall reliability of gas turbines. When equipped with better monitoring and controls, power plants can operate with increased fuel-burning efficiency, improved process dynamics and gas concentrations, and increased overall longevity of the power plant components. This will result in increased turbine availability and a reduction in outages and maintenance costs. One of the key aspects to driving forward turbine monitoring capability is the development of high temperature capable integrated circuit (IC) electronics. Previous papers have described 500 °C + electronics that were developed primarily from a combination of discrete single transistors combined with supporting high temperature passive components. While these circuits have been tested successfully in high temperature spin test environments, the move to an IC approach will greatly increase the performance and reliability of turbine monitoring systems. This program is developing such capability through the implementation of silicon carbide (SiC) based ICs, and this paper details the initial approach and early testing of the developed devices. This research represents an important step towards the realization of a field deployable high reliability turbine condition monitoring system.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Automated Dynamic Demand Response Implementation on a Micro-grid

In this paper, we describe a system for real-time automated Dynamic and Sustainable Demand Response with sparse data consumption prediction implemented on the University of Southern California campus microgrid. Supply side approaches to resolving energy supply-load imbalance do not work at high levels of renewable energy penetration. Dynamic Demand Response (D 2 R) is a widely used demand-side technique to dynamically adjust electricity consumption during peak load periods. Our D 2 R system consists of accurate machine learning based energy consumption forecasting models that work with sparse data coupled with fast and sustainable load curtailment optimization algorithms that provide the ability to dynamically adapt to changing supply-load imbalances in near real-time. Our Sustainable DR (SDR) algorithms attempt to distribute customer curtailment evenly across sub-intervals during a DR event and avoid expensive demand peaks during a few sub-intervals. It also ensures that each customer is penalized fairly in order to achieve the targeted curtailment. We develop near linear-time constant-factor approximation algorithms along with Polynomial Time Approximation Schemes (PTAS) for SDR curtailment that minimizes the curtailment error defined as the difference between the target and achieved curtailment values. Our SDR curtailment problem is formulated as an Integer Linear Program that optimally matches customers to curtailment strategies during a DR event while also explicitly accounting for customer strategy switching overhead as a constraint. We demonstrate the results of our D 2 R system using real data from experiments performed on the USC smartgrid and show that 1) our prediction algorithms can very accurately predict energy consumption even with noisy or missing data and 2) our curtailment algorithms deliver DR with extremely low curtailment errors in the 0.01-0.05 kWh range.

Kuppannagari, Sanmukh R.↗
DOE OSTI2019

Molecular Simulation of the Catalytic Regeneration of n BuLi through a Hydrometalation Route

Efficient regeneration of organolithium compounds is a challenging aspect in the process of novel organometathetical catalytic cycles. A new method for Mg production from seawater has been proposed that capitalizes on the rich chemistry of Grignard reagents. Based on a 3-step catalytic cycle with Cp2MClL catalyst (M=Ti, Zr and L select organic ligands), it requires the regeneration of nBuLi. The potential of the proposed approach is evaluated with density functional theory based molecular simulations. The results reveal that the high affinity of Li towards Cl and N results in the formation of alkanes, and the strong coupling of catalyst and BuLi leads to catalyst deactivation. To improve its catalytic performance, we proposed the use of a diamine co-catalyst and a modified catalyst with a ligand that does not contain N, which would help release BuLi from the vicinity of the catalyst. Ab inito molecular dynamics simulations at 298 K in explicit solvent (THF) were used to estimate the Gibb’s free energetics and equilibrium constants obtained from the vibrational density of states. The results show a marked improvement in the free-energetics with lower barriers towards the completion of the catalytic cycle and suppression of de-activation channels. M.-S. L. and B. P. M. were supported by the US Department of Energy (DOE), Advanced Research Projects Agency for Energy performed at the Pacific Northwest National Laboratory (PNNL). V.-A. G. and R. R. were supported by DOE, Office of Science, Office of Basic Energy Sciences-CGBS Catalysis Program. Computational resources were provided by PNNL Research Computing Cluster and the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231.

Lee, Mal-Soon (ORCID:000000016851177X)↗
DOE OSTI2016

High-performance epistasis detection in quantitative trait GWAS

epiSNP is a program for identifying pairwise single nucleotide polymorphism (SNP) interactions (epistasis) in quantitative-trait genome-wide association studies (GWAS). A parallel MPI version (EPISNPmpi) was created in 2008 to address this computationally expensive analysis on large data sets with many quantitative traits and SNP markers. However, the falling cost of genotyping has led to an explosion of large-scale GWAS data sets that challenge EPISNPmpi’s ability to compute results in a reasonable amount of time. Therefore, we optimized epiSNP for modern multi-core and highly parallel many-core processors to efficiently handle these large data sets. This paper describes the serial optimizations, dynamic load balancing using MPI-3 RMA operations, and shared-memory parallelization with OpenMP to further enhance load balancing and allow execution on the Intel Xeon Phi coprocessor (MIC). For a large GWAS data set, our optimizations provided a 38.43× speedup over EPISNPmpi on 126 nodes using 2 MICs on TACC’s Stampede Supercomputer. We also describe a Coarray Fortran (CAF) version that demonstrates the suitability of PGAS languages for problems with this computational pattern. We show that the Coarray version performs competitively with the MPI version on the NERSC Edison Cray XC30 supercomputer. Finally, the performance benefits of hyper-threading for this application on Edison (average 1.35× speedup) are demonstrated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

VERA-CS Modeling and Simulation of PWR Main Steam Line Break Core Response to DNB

The Virtual Environment for Reactor Applications core simulator (VERA-CS) being developed by the Consortium for the Advanced Simulation of Light Water Reactors (CASL) includes coupled neutronics, thermal-hydraulics, and fuel temperature components with an isotopic depletion capability. The neutronics capability employed is based on MPACT, a three-dimensional (3-D) whole core transport code. The thermal-hydraulics and fuel temperature models are provided by the COBRA-TF (CTF) subchannel code. As part of the CASL development program, the VERA-CS (MPACT/CTF) code system was applied to model and simulate reactor core response with respect to departure from nucleate boiling ratio (DNBR) at the limiting time step of a postulated pressurized water reactor (PWR) main steamline break (MSLB) event initiated at the hot zero power (HZP), either with offsite power available and the reactor coolant pumps in operation (high-flow case) or without offsite power where the reactor core is cooled through natural circulation (low-flow case). The VERA-CS simulation was based on core boundary conditions from the RETRAN-02 system transient calculations and STAR-CCM+ computational fluid dynamics (CFD) core inlet distribution calculations. The evaluation indicated that the VERA-CS code system is capable of modeling and simulating quasi-steady state reactor core response under the steamline break (SLB) accident condition, the results are insensitive to uncertainties in the inlet flow distributions from the CFD simulations, and the high-flow case is more DNB limiting than the low-flow case.

CTF↗
DOE OSTI2020

Programmable topotaxis of magnetic rollers in time-varying fields

We discuss how spatially uniform, time-periodic magnetic fields can be designed to power and direct the migration of ferromagnetic spheres up (or down) local gradients in the topography of a solid substrate. Our results are based on a dynamical model that considers the time-varying magnetic torques on the particle and its motion through the fluid at low Reynolds number. We use both analytical theory and numerical simulation to design magnetic fields that maximize the migration velocity up (or down) an inclined plane. We show how “topotaxis” of spherical particles relies on differences in the hydrodynamic resistance to rotation about axes parallel and perpendicular to the plane. Importantly, the designed fields can drive multiple independent particles to move simultaneously in different directions as determined by gradients in their respective environments. Experiments on ferromagnetic spheres provide evidence for topotactic motions up inclined substrates. The ability to program the autonomous navigation of driven particles within anisotropic environments is relevant to the design of colloidal robots.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

High reliable and stable organic field-effect transistor nonvolatile memory with a poly(4-vinyl phenol) charge trapping layer based on a pn-heterojunction active layer

In this letter, we demonstrate a high reliable and stable organic field-effect transistor (OFET) based nonvolatile memory (NVM) with a polymer poly(4-vinyl phenol) (PVP) as the charge trapping layer. In the unipolar OFETs, the inreversible shifts of the turn-on voltage (V{sub on}) and severe degradation of the memory window (ΔV{sub on}) at programming (P) and erasing (E) voltages, respectively, block their application in NVMs. The obstacle is overcome by using a pn-heterojunction as the active layer in the OFET memory, which supplied a holes and electrons accumulating channel at the supplied P and E voltages, respectively. Both holes and electrons transferring from the channels to PVP layer and overwriting the trapped charges with an opposite polarity result in the reliable bidirectional shifts of V{sub on} at P and E voltages, respectively. The heterojunction OFET exhibits excellent nonvolatile memory characteristics, with a large ΔV{sub on} of 8.5 V, desired reading (R) voltage at 0 V, reliable P/R/E/R dynamic endurance over 100 cycles and a long retention time over 10 years.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Summary of Development for Structural Component Modeling in Fiscal Year 2022

This report summarizes efforts performed during Fiscal Year 2022 to develop capabilities for modeling structural component degradation in support of the U.S. Department of Energy's Nuclear Energy Advanced Modeling and Simulation Program. These efforts were centered around development of capabilities for the Grizzly code. Efforts focused both on foundational engineering-scale analysis capabilities for damage and shell formulations and on material-scale tools for accounting for irradiation effects in Grade 91 steel. A major outcome of this effort was the development of a general-purpose tool for nonlocal averaging of material properties, which enabled nonlocal damage models. In addition, the applicability of the shell elements in MOOSE has been expanded to allow modeling a wider range of component geometries and using a wide variety of material models. For irradiation effects on materials, a previously developed cluster dynamics model for light water reactor pressure vessel steels has been adapted for application to Grade 91 alloy. This work builds on prior efforts to build a flexible, capable code for addressing a variety of aging and component performance issues in nuclear power plant structural components.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Identification of small molecule inhibitors of botulinum neurotoxin serotype E via footprint similarity

Botulinum neurotoxins (BoNT) are among the most poisonous substances known, and of the 7 serotypes (A–G) identified thus far at least 4 can cause death in humans. Here, the goal of this work was identification of inhibitors that specifically target the light chain catalytic site of the highly pathogenic but lesser-studied E serotype (BoNT/E). Large-scale computational screening, employing the program DOCK, was used to perform atomic-level docking of 1.4 million small molecules to prioritize those making favorable interactions with the BoNT/E site. In particular, ‘footprint similarity’ (FPS) scoring was used to identify compounds that could potentially mimic features on the known substrate tetrapeptide RIME. Among 92 compounds purchased and experimentally tested, compound C562-1101 emerged as the most promising hit with an apparent IC 50 value three-fold more potent than that of the first reported BoNT/E small molecule inhibitor NSC-77053. Additional analysis showed the predicted binding pose of C562-1101 was geometrically and energetically stable over an ensemble of structures generated by molecular dynamic simulations and that many of the intended interactions seen with RIME were maintained. Finally, several analogs were also computationally designed and predicted to have further molecular mimicry thereby demonstrating the potential utility of footprint-based scoring protocols to help guide hit refinement.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

User defined elements in ANSYS for 2D multiphysics modeling of superconducting magnets

Dynamic simulation of superconducting magnets is critical for the design of quench protection systems to prevent potentially damaging temperatures and high voltage from developing after magnet quench. Modeling these scenarios is challenging due to the many multiscale phenomena which impact magnet behavior. These range from conductor scale effects of quench and interfilament coupling currents up to the behavior of the magnet in its powering and protection circuit. In addition, a strong coupling between electromagnetic and thermal domains is required to capture temperature and field dependent material properties and quench behavior. We present a finite element approach which integrates the various effects into the commercial software ANSYS by means of programming new element types. This is shown capable of simulating the strongly coupled transient electromagnetic, thermal, and circuit behavior of superconducting magnets required for quench protection studies. A benchmarking study is presented which shows close agreement between the new ANSYS elements and a COMSOL Multiphysics implementation developed at CERN for dump resistor and coupling loss induced quench based magnet protection of a Nb 3 Sn block dipole. Following this, the ANSYS implementation is shown reproducing strongly coupled quench back behavior observed during the test of a Nb 3 Sn superconducting undulator prototype at Lawrence Berkeley National Laboratory.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Journey over Destination: Dynamic Sensor Placement Enhances Generalization

Reconstructing complex, high-dimensional global fields from limited data points is a challenge across various scientific and industrial domains. This is particularly important for recovering spatio-temporal fields using sensor data from, for example, laboratory-based scientific experiments, weather forecasting, or drone surveys. Given the prohibitive costs of specialized sensors and the inaccessibility of
certain regions of the domain, achieving full field coverage is typically not feasible. Therefore, the development of machine learning algorithms trained to reconstruct fields given a limited dataset is of critical importance. In this study, we introduce a general
approach that employs moving sensors to enhance data exploitation during the training of an attention based neural network, thereby improving field reconstruction. The training of sensor locations is accomplished using an end-to-end workflow, ensuring
differentiability in the interpolation of field values associated to the sensors, and is simple to implement using differentiable programming. Additionally, we have incorporated a correction mechanism to prevent sensors from entering invalid regions within the domain. We evaluated our method using two distinct datasets; the results show that our approach enhances learning, as evidenced by improved test scores.

54 ENVIRONMENTAL SCIENCES↗