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All sources NASA NTRS DOE OSTI

DOE OSTI2022

Bringing chemical structures to life with augmented reality, machine learning, and quantum chemistry

Visualizing 3D molecular structures is crucial to understanding and predicting their chemical behavior. However, static 2D hand-drawn skeletal structures remain the preferred method of chemical communication. Here, we combine cutting-edge technologies in augmented reality (AR), machine learning, and computational chemistry to develop MolAR, an open-source mobile application for visualizing molecules in AR directly from their hand-drawn chemical structures. Users can also visualize any molecule or protein directly from its name or protein data bank ID and compute chemical properties in real time via quantum chemistry cloud computing. MolAR provides an easily accessible platform for the scientific community to visualize and interact with 3D molecular structures in an immersive and engaging way.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Special Issue: To Commemorate the Legacy of Malcolm J. Andrews—Scientist, Educator, and Leader

This special issue celebrates the scientific legacy of Dr. Malcolm J. Andrews, former Editor of the ASME Journal of Fluids Engineering (2010–2018). Malcolm devoted his career to the development and applications of computational, experimental, and analytical tools to investigate and solve diverse, complex fluid dynamics, and heat transfer problems. Malcolm’s most notable research accomplishments include: (i) contributions to innovative experiments, simulations, and theory to understand buoyancydriven turbulent mixing due to Rayleigh–Taylor instability; (ii) development of the multiphase particle-in-cell method for understanding fragmentation in spray jets, coal-water slurry, and other dense particulate flows; (iii) mixing in nano- and biomaterials in applications ranging from protein glues to carbon nanotube composites; (iv) determining dispersion in proteins to improve therapeutic drug delivery via micro-encapsulation; (v) innovative methods for storing information using nanopinned dipole magnetic vortices in superconducting materials; and (vi) modeling and design modifications for shell-and-tube heat exchangers, as well as heat transfer in rotating systems such as turbine blades. The results of his research have been published in a long series of trend-setting journal and conference papers, book chapters, and patents.

42 ENGINEERING↗

DOE OSTI2025

Bias-Variance Trade-Off in Physics-Informed Neural Networks with Randomized Smoothing for High-Dimensional PDEs

Physics-Informed Neural Networks (PINNs) have triggered a paradigm shift in scientific computing, leveraging mesh-free properties and robust approximation capabilities. While proving effective for low-dimensional partial differential equations (PDEs), the computational cost of PINNs remains a hurdle in high-dimensional scenarios. This is particularly pronounced when computing high-order and high-dimensional derivatives in the physics-informed loss. Randomized Smoothing PINN (RS-PINN) introduces Gaussian noise for stochastic smoothing of the original neural net model, enabling the use of Monte Carlo methods for derivative approximation, which eliminates the need for costly automatic differentiation. Despite its computational efficiency, especially in the approximation of high-dimensional derivatives, RS-PINN introduces biases in both loss and gradients, negatively impacting convergence, especially when coupled with stochastic gradient descent (SGD) algorithms. We present a comprehensive analysis of biases in RS-PINN, attributing them to the nonlinearity of the Mean Squared Error (MSE) loss as well as the intrinsic nonlinearity of the PDE itself. We propose tailored bias correction techniques, delineating their application based on the order of PDE nonlinearity. The derivation of an unbiased RS-PINN allows for a detailed examination of its advantages and disadvantages compared to the biased version. Specifically, the biased version has a lower variance and runs faster than the unbiased version, but it is less accurate due to the bias. To optimize the bias-variance trade-off, we combine the two approaches in a hybrid method that balances the rapid convergence of the biased version with the high accuracy of the unbiased version. In addition to methodological contributions, we present an enhanced implementation of RS-PINN. Extensive experiments on diverse high-dimensional PDEs, including Fokker-Planck, Hamilton-Jacobi-Bellman (HJB), viscous Burgers’, Allen-Cahn, and Sine-Gordon equations, illustrate the bias-variance trade-off and highlight the effectiveness of the hybrid RS-PINN. Empirical guidelines are provided for selecting biased, unbiased, or hybrid versions, depending on the dimensionality and nonlinearity of the specific PDE problem.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Kraken2 Metagenomic Virus Database

The Database: Kraken2 [1] database built from a classification tree containing over 700k metagenomic viruses from JGI IMG/VR [2]. (1) Wood, D. E., Lu, J., and Langmead, B. (2019). Improved metagenomic analysis with Kraken 2. Genome Biol., 20(1), 1â€“13. doi: 10.1186/s13059-019-1891-0 (2) Paez-Espino D, Chen I-MA, Palaniappan K, Ratner A, Chu K, Szeto E, et al. IMG/VR: a database of cultured and uncultured DNA Viruses and retroviruses. Nucleic Acids Res. 2017;45:D457-65. For Paper: Title: A k-mer based approach for virus classification in metatranscriptomic and metagenomic samples identifies viral associations in the Populus phytobiome and autism brains Abstract Background Viruses are an underrepresented taxa in the study and identification of microbiome constituents; however, they play an important role in health, microbiome regulation, and transfer of genetic material. Only a few thousand viruses have been isolated, sequenced, and assigned a taxonomy, which further limits the ability to identify and quantify viruses in the microbiome. Additionally, the vast diversity of viruses represents a challenge for classification, not only in constructing a viral taxonomy, but also in identifying similarities between a virus' genotype and its phenotype. However, the diversity of viral sequences can be leveraged to classify their sequences in metagenomic and metatranscriptomic samples. Methods To identify viruses in transcriptomic and genomic samples, we developed a dynamic programming algorithm for creating a classification tree out of 715,672 metagenome viruses. To create the classification tree, we clustered proportional similarity scores generated from the k-mer profiles of each of the metagenome viruses. We then integrated the viral classification tree with the NCBI taxonomy for use with ParaKraken, a metagenomic/transcriptomic classifier. Results To illustrate the breadth of our utility for classifying viruses with ParaKraken, we analyzed data from a plant metagenome study identifying the differences between two Populus genotypes in three different compartments and on a human metatranscriptome study identifying the differences between Autism Spectrum Disorder patients and controls in post mortem brain biopsies. In the Populus study, we identified genotype and compartment specific viral signatures, while in the Autism study we identified a significant increased abundance of eight viral sequences in Autism brain biopsies. Conclusion Viruses represent an important aspect of the microbiome. The ability to classify viruses represents the first step in being able to better understand their role in the microbiome. The viral classification method presented here allows for more complete identification of viral sequences for use in identifying associations between viruses and the host and viruses and other microbiome members. Acknowledgements and Funding This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. This research was also supported by the Plant-Microbe Interfaces Scientific Focus Area in the Genomic Science Program, the Office of Biological and Environmental Research (BER) in the U.S. Department of Energy Office of Science, and by the Department of Energy, Laboratory Directed Research and Development funding (ProjectID 8321), at the Oak Ridge National Laboratory. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the US DOE under contract DE-AC05-00OR22725. This research used resources of the Compute and Data Environment for Science (CADES).

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Risk-Informed MultiPphysics BestEestimate Plus Uncertainties (BEPU) Application Development of RELAP5-3D Perturbation Model

The United States nuclear industry is facing a strong challenge to ensure maximum safety while enhancing economic benefit. Safety is a key parameter to all aspects related to light water reactor (LWR) nuclear power plants (NPPs), especially cost savings. Since the goal is to extend the lifetimes of these NPPs, the traditional deterministic safety concept may not guarantee a current economic asset. The Light Water Reactor Sustainability (LWRS) Program has been promoting a wide range of research and development (R&D) in this field to maximize the safety, economics, and performance of these NPPs through improved scientific understanding. One of the best practices to achieve this goal is to identify and optimize safety margins, which can lead to cost reduction. To do this, under the LWRS framework, the Risk-Informed Systems Analysis (RISA) Pathway will focus on the optimization of safety margin and minimization of uncertainties to ensure both safety and economics at the highest level. The RISA Pathway will provide enhanced capabilities for analyzing and characterizing LWR systems performance by developing and demonstrating methods, tools, and data to enable risk-informed margins management (RIMM). The goals of the RISA Pathway are twofold: (1) deploy the risk-informed tools and methods that enable better representation of safety margins and factors that contribute to cost and safety; and (2) conduct advanced risk assessment applications with industry to support margin management strategies that enable more cost-effective plant operation. The tools and methods provided by the RISA Pathway will support effective margin management for both active and passive safety systems, structures, and components (SSC) of an NPP. The tools and methods used in the RISA Pathway should have high confidence and highest technical maturity for and implementation to industry at its current setting. They should also have a capability to support risk-informed decision making for both probabilistic and deterministic elements of safety. The RISA Pathway will, therefore, perform a comprehensive assessment of verification and validation (V&V) status of RISA Toolkit to enhance credibility RISA Toolkit which be used by industry. This report summarizes RELAP5-3D development activities for best estimated and uncertainty (BEPU) capability to support ongoing RISA Pathway pilot projects for risk informed uncertainty quantification applications. Work scope includes (1) development of initial perturbation model for selected closure laws and (2) testing of suitable probability distribution functions (PDF) for the selected correlations. Developed BEPU modules are tested with selected separate effect cases and compared with conventional thermal-hydraulics model.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Using interfaces to: create strongly-coupled magnetic-ferroelectrics

Starting at the level of electrons and atoms our long-term goal is to rationally design complex oxide heterostructures and interface-materials with targeted emergent behaviors. This is not a matter of simply optimizing material parameters, but rather begins with understanding a mechanism to control the interplay between the diverse microscopic degrees of freedom prevalent in complex oxides in order to create targeted macroscopic phenomena and ends with the design of new material realizations. These realizations are in turn created with atomic-layer precision, structurally assessed to see that they are the intended realization, and finally their relevant properties are measured. During this program we have developed the scientific ideas necessary to apply this materials-by-design paradigm to the creation of materials offering electrical control of magnetism. At the heart of our approach lies a partnership between theory, synthesis, and characterization. These are the areas of expertise of the three co-PIs, who have worked closely together since their arrival at Cornell. During this project bold ideas on where to put the atoms to provide electrical control of magnetism were met with the latest methods of synthesis science and electron microscopy in an attempt create targeted new multiferroics. Our work applying this materials-by-design methodology to multiferroics has enjoyed multiple successes. The Co-PIs have invented new mechanisms for multiferroics, which provide strong coupling between polarization and magnetism, and reduced them to practice. Specifically, we pioneered multiferroics based on spin-phonon coupling, rotation-driven multiferroicity, and novel geometric multiferroics. Importantly, the latter two of these mechanisms enable the deterministic switching of magnetism by an applied electric field, which is key to the application of multiferroics to future device technologies. Most recently, we performed detailed theoretical analysis on a system involving the combination of magnetoelectric and colossal magnetoresistance behaviors and predict that electrical control of a metal-insulator transition is possible in appropriately strained SmBaMn2O6. The research enabled by this grant led to 63 publications in leading refereed journals including Nature, Nature Materials, Physical Review Letters, and Advanced Materials demonstrating a materials-by-design paradigm to the creation of materials offering electrical control of magnetism.

36 MATERIALS SCIENCE↗

NASA NTRS1995

Setting priorities for space research: An experiment in methodology

In 1989, the Space Studies Board created the Task Group on Priorities in Space Research to determine whether scientists should take a role in recommending priorities for long-term space research initiatives and, if so, to analyze the priority-setting problem in this context and develop a method by which such priorities could be established. After answering the first question in the affirmative in a previous report, the task group set out to accomplish the second task. The basic assumption in developing a priority-setting process is that a reasoned and structured approach for ordering competing initiatives will yield better results than other ways of proceeding. The task group proceeded from the principle that the central criterion for evaluating a research initiative must be its scientific merit -- the value of the initiative to the proposing discipline and to science generally. The group developed a two-stage methodology for priority setting and constructed a procedure and format to support the methodology. The first of two instruments developed was a standard format for structuring proposals for space research initiatives. The second instrument was a formal, semiquantitative appraisal procedure for evaluating competing proposals. This report makes available complete templates for the methodology, including the advocacy statement and evaluation forms, as well as an 11-step schema for a priority-setting process. From the beginning of its work, the task group was mindful that the issue of priority setting increasingly pervades all of federally supported science and that its work would have implications extending beyond space research. Thus, although the present report makes no recommendations for action by NASA or other government agencies, it provides the results of the task group's work for the use of others who may study priority-setting procedures or take up the challenge of implementing them in the future.

Source record↗

NASA NTRS2008

The Evolution of Deep Space Navigation: 1989-1999

The exploration of the planets of the solar system using robotic vehicles has been underway since the early 1960s. During this time the navigational capabilities employed have increased greatly in accuracy, as required by the scientific objectives of the missions and as enabled by improvements in technology. This paper is the second in a chronological sequence dealing with the evolution of deep space navigation. The time interval covered extends from the 1989 launch of the Magellan spacecraft to Venus through a multiplicity of planetary exploration activities in 1999. The paper focuses on the observational techniques that have been used to obtain navigational information, propellant-efficient means for modifying spacecraft trajectories, and the computational methods that have been employed, tracing their evolution through a dozen planetary missions.

Magellan↗

NASA NTRS2012

Eclipse-Free-Time Assessment Tool for IRIS

IRIS_EFT is a scientific simulation that can be used to perform an Eclipse-Free- Time (EFT) assessment of IRIS (Infrared Imaging Surveyor) mission orbits. EFT is defined to be those time intervals longer than one day during which the IRIS spacecraft is not in the Earth s shadow. Program IRIS_EFT implements a special perturbation of orbital motion to numerically integrate Cowell's form of the system of differential equations. Shadow conditions are predicted by embedding this integrator within Brent s method for finding the root of a nonlinear equation. The IRIS_EFT software models the effects of the following types of orbit perturbations on the long-term evolution and shadow characteristics of IRIS mission orbits. (1) Non-spherical Earth gravity, (2) Atmospheric drag, (3) Point-mass gravity of the Sun, and (4) Point-mass gravity of the Moon. The objective of this effort was to create an in-house computer program that would perform eclipse-free-time analysis. of candidate IRIS spacecraft mission orbits in an accurate and timely fashion. The software is a suite of Fortran subroutines and data files organized as a "computational" engine that is used to accurately predict the long-term orbit evolution of IRIS mission orbits while searching for Earth shadow conditions.

Eagle, David↗

NASA NTRS2014

Multi-Source Autonomous Response for Targeting and Monitoring of Volcanic Activity

The study of volcanoes is important for both purely scientific and human survival reasons. From a scientific standpoint, volcanic gas and ash emissions contribute significantly to the terrestrial atmosphere. Ash depositions and lava flows can also greatly affect local environments. From a human survival standpoint, many people live within the reach of active volcanoes, and therefore can be endangered by both atmospheric (ash, debris) toxicity and lava flow. There are many potential information sources that can be used to determine how to best monitor volcanic activity worldwide. These are of varying temporal frequency, spatial regard, method of access, and reliability. The problem is how to incorporate all of these inputs in a general framework to assign/task/reconfigure assets to monitor events in a timely fashion. In situ sensing can provide a valuable range of complementary information such as seismographic, discharge, acoustic, and other data. However, many volcanoes are not instrumented with in situ sensors, and those that have sensor networks are restricted to a relatively small numbers of point sensors. Consequently, ideal volcanic study synergistically combines space and in situ measurements. This work demonstrates an effort to integrate spaceborne sensing from MODIS (Terra and Aqua), ALI (EO-1), Worldview-2, and in situ sensing in an automated scheme to improve global volcano monitoring. Specifically, it is a "sensor web" concept in which a number of volcano monitoring systems are linked together to monitor volcanic activity more accurately, and this activity measurement automatically tasks space assets to acquire further satellite imagery of ongoing volcanic activity. A general framework was developed for evidence combination that accounts for multiple information sources in a scientist-directed fashion to weigh inputs and allocate observations based on the confidence of an events occurrence, rarity of the event at that location, and other scientists' inputs. The software framework uses multiple source languages and is a general framework for combining inputs and incrementally submitting observation requests/reconfigurations, accounting for prior requests. The autonomous aspect of operations is unique, especially in the context of the wide range of inputs that includes manually inputted electronic reports (such as the Air Force Weather Advisories), automated satellite-based detection methods (such as MODVOLC and GOESVOLC), and in situ sensor networks.

Davies, Ashley G.↗

NASA NTRSDate not supplied

Linear Stokes Measurement of Thermal Targets using Compact LWIR Spectropolarimeter

The trade-o↵ between spectral resolution and instrument throughput is analyzed for a compact, uncooled, longwave infrared (LWIR) channeled spectropolarimeter (IRCSP). The IRCSP was developed as a part of the Submm Wave and InfraRed Polarimeters (SWIRP) project out of NASA’s Goddard Spaceflight Center. The IRCSP scientific objective targets measurements of AOLP and DOLP with 1-μm spectral resolution from 8.5 - 12.5 μm in a single snapshot. The geometry of the field stop determines the field of view (FOV) of the IRCSP. This work relates the spectral resolution, instrument throughput, and polarimetric accuracy of a spectro-polarimeter to the FOV. The accuracy of linear Stokes retrievals for low temperature thermal targets are predicted for varying FOV and measurement noise conditions. This work presents a method to quantify the achievable accuracy in AOLP and DOLP as a function of field stop dimensions and signal-to-noise ratio (SNR). While smaller field stops are shown to improve accuracy as the spectral resolution is increased, low SNR is the dominant source of error for the IRCSP prototype. For the IRCSP, a SNR of at least 80 is required to produce DOLP measurements with < 5% error for targets with DOLP < 0.2.

LWIR↗

NASA NTRSDate not supplied

Safety Expertise and the Perils of Novelty

Emerging aviation markets such as urban air mobility are giving rise to new technologies and means of operation. However, novelty may hide ‘unknown unknowns,’ raising new hazards. This paper examines how expertise and safety techniques enable transformative technologies such as reduced crew operations, hybrid wing-borne and rotor-born flight, federated air traffic services, and urban operations. We explore how analysts use expertise to address common-cause failures, collect and interpret safety data, and perform exacting tradeoffs between dissimilarity, redundancy, independence, and diversity (human, process lifecycle, or otherwise) to ensure safety. When novelty is present, analysts might not possess the expertise needed to fully understand the implications of design decisions and tradeoffs being made, especially in early lifecycle phases, on emergent properties such as safety. Safety expertise must be carefully cultivated. The conflicting views of safety experts must be unpacked to identify the divergence in fundamental assumptions, models, means, and methods that may be causing them. Once systems venture beyond the basis of what safety expertise can reliably guarantee, projects take on risk that must be managed. The paper contains key takeaways and actionable recommendations for novel OEMs and regulators touching on topics such as robust monitoring; clear and transparent reporting; incremental approaches to fielding novel systems in hazard-rich, risk-tolerant environments; the cultivation of safety culture and expertise in an organization; and the use of scientific study to reduce epistemic uncertainty in novel operations with new technologies. Since excessive novelty in aviation can undermine the current foundation of safety, humility and incrementalism are necessary to enable emerging aviation markets safely.

safety expertise↗

NASA NTRSDate not supplied

Safety Expertise and the Perils of Novelty

safety expertise↗

NASA NTRS1995

Development of Distant Learning Laboratory and Creation of Educational Materials

The Office of Education's fundamental goal is to disseminate information, mostly that which relates to science and technology. In this attempt, as I have observed, the office has many programs bringing both students and teachers to NASA Langley to expose them to the facilities and to teach them some about the scientific theory and about available modern technology. As a way of expanding the audience that can be reached, as the expense of bringing people in is limiting, Marchelle Canright has proposed establishing a center dedicated to researching and producing distant learning videos. Although distant learning through telecommunications is not a new concept, as many universities, colleges, and precollege level schools offer televised courses, the research in this field has been limited. Many of the standing distant learning broadcasts are simply recordings of teachers in classrooms giving lectures to their own students; they are not aimed at the television audience. In some cases the videos are produced without a Live-lecture atmosphere, but are still only classroom lectures. In either case, however, the full range of capabilities of video production are not being fully utilized. Methods for best relaying educational material have not been explored. Possibilities for including computerized images and video clips for the purpose of showing diagrams and processes, as well as examples in fitting cases, may add considerably to the educational value of these videos. Also, through Internet and satellite links, it is possible for remote students to interact with the teachers during televised sessions. These possibilities might, also, add to the effectiveness of distant learning programs. Ms. Canright's proposed center will be dedicated to researching these possibilities and eventually spreading the results to distant learning program managers. This is the project I was involved in over the summer. As implied, the center is still at the foundation stages. Ms. Canright has proposed four or five possible series that could be developed, each one aimed at a specific age group of students, or group of teachers. I was involved in the design of the series aimed at the youngest children, the Picture Book Science series. My involvement included proposing and researching topics, writing a lesson for the first show, writing the latter portion of the picture book story (the part including the scientific lesson), and illustrating the story. I also designed and collected the materials for the Learning Center's television studio set as well as finished the painting of the main backdrop panels.

Considine, Michelle↗

NASA NTRS1972

Design principles for contamination abatement in scientific satellites.

It is shown that deposition of contamination films on satellite optics can be controlled by the following means: isolating critical optical surfaces from the rest of the spacecraft; avoiding or minimizing the use of nonmetallic material, particularly near or in line of sight of optical surfaces; avoiding materials with high vapor pressures; subjecting materials to vacuum baking prior to use, to drive off the volatile outgassing products; keeping the critical surfaces at temperatures above the ambient; avoiding elevated operational temperatures for nonmetallic materials; paying special attention to optics exposed to intense UV-, X-ray, or particular radiation; avoiding water-vapor sources; and directing RCS plumes away from critical surfaces. Methods of controlling particulate contaminants are also proposed.

Naumann, R. J.↗

DOE OSTI2022

Understanding the Impact of Data Staging for Coupled Scientific Workflows

We report the rate of data generated by cutting-edge experimental science facilities and large-scale simulations enabled by current high-performance computing (HPC) systems has continued to grow at a far greater pace than the development of the network and storage capabilities on which these systems rely. To cope with this challenge, scientist are moving toward the creation of autonomous experiments and HPC simulations using machine learning. However, efficiently moving, storing, and processing large amounts of data away from the point of origin presents an incredible challenge. In-memory computing, in situ analysis, data staging, and data streaming are recognized viable alternatives to traditional file-based methods for transferring data between coupled workflows. However, the performance trade-offs and limitations for these methods are not fully understood when used in HPC applications. This article presents a comprehensive performance assessment of the current solutions for data staging when applied to applications that are not necessary I/O intensive which makes them not ideal candidates for these methods. Our study is based on experiments running at scale on Oak Ridge National Laboratory's Summit supercomputer using applications and simulations that cover typical computational motifs and patterns. We investigated the usability and cost/benefit trade-offs of staging algorithms for HPC applications under different scenarios and highlight opportunities for optimizing the dataflow between coupled simulation workflows.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Extracting meaningful standard enthalpies and entropies of activation for surface reactions from kinetic rates

While analyses based on the Arrhenius kinetic model have been successfully applied since its introduction, the Arrhenius model neglects to describe pre-exponential factor in a scientifically meaningful fashion. Since the 1930s, transition-state theory (TST), has met with success in interpreting the pre-exponential factor’s value, allowing a standard entropy of activation to be estimated. However, analyses based on TST’s assumptions have been applied inconsistently in the literature, particularly in corrosion science, leading to difficulty in comparison of standard entropy of activations from different studies. In this work, the foundational principles of TST and standard states are discussed and a standard method to apply TST in analyzing rates of surface reactions is recommended. When full details are not available, reacting species’ concentrations should be normalized to the concentration of active surface sites. For corrosion reactions, conversion relationships are given to convert from units of corrosion rate to surface reaction rate, consistent with TST. This method is dubbed a surface reactant equi-density approximation. Application of this standard to reported data results in adjustments of the standard entropy of activation between - 65 and +50 J/mol K and brings reported entropies into a narrower range of values.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Machine learning for reparameterization of multi-scale closures

Scientific machine learning (ML) is becoming increasingly useful in learning closure models for multi-scale physics problems; however, many ML approaches require a vast array of training data and can struggle with generalization and interpretability. Here, rather than learning an entire closure operator, we adopt an existing reduced-dimension model of the microphysics and learn an optimal re-parameterization of the solver. We demonstrate two approaches for training the reduced dimension closure model (1) an a priori method that optimizes the closure parameterization and the neural network parameters separately and (2) an a posteriori method that simultaneously optimizes both. Using the simulation of biomass pyrolysis as a motivating example, we show that the a posteriori method achieves better target losses and is less dependent on training dataset size for generalizability. We then demonstrate the impact that implementing this reparameterization has at the macroscale, showing improved predictive performance with no modification to the underlying macroscale solvers.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗