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DOE OSTI2024

Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics

Abstract Advancements in computing power have recently made it possible to utilize machine learning and deep learning to push scientific computing forward in a range of disciplines, such as fluid mechanics, solid mechanics, materials science, etc. The incorporation of neural networks is particularly crucial in this hybridization process. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data are sparse, which is the case in many scientific and engineering domains. Nonetheless, neural networks provide a solid foundation to respect physics-driven or knowledge-based constraints during training. Generally speaking, there are three distinct neural network frameworks to enforce the underlying physics: (i) physics-guided neural networks (PgNNs), (ii) physics-informed neural networks (PiNNs), and (iii) physics-encoded neural networks (PeNNs). These methods provide distinct advantages for accelerating the numerical modeling of complex multiscale multiphysics phenomena. In addition, the recent developments in neural operators (NOs) add another dimension to these new simulation paradigms, especially when the real-time prediction of complex multiphysics systems is required. All these models also come with their own unique drawbacks and limitations that call for further fundamental research. This study aims to present a review of the four neural network frameworks (i.e., PgNNs, PiNNs, PeNNs, and NOs) used in scientific computing research. The state-of-the-art architectures and their applications are reviewed, limitations are discussed, and future research opportunities are presented in terms of improving algorithms, considering causalities, expanding applications, and coupling scientific and deep learning solvers.

Computer Science↗

DOE OSTI2022

Atomic step disorder on polycrystalline surfaces leads to spatially inhomogeneous work functions

Structural disorder causes materials’ surface electronic properties, e.g., work function (ϕ), to vary spatially, yet it is challenging to prove exact causal relationships to underlying ensemble disorder, e.g., roughness or granularity. For polycrystalline Pt, nanoscale resolution photoemission threshold mapping reveals a spatially varying ϕ=5.70±0.03 eV over a distribution of (111) vicinal grain surfaces prepared by sputter deposition and annealing. With regard to field emission and related phenomena, e.g., vacuum arc initiation, a salient feature of the ϕ distribution is that it is skewed with a long tail to values down to 5.4 eV, i.e., far below the mean, which is exponentially impactful to field emission via the Fowler–Nordheim relation. We show that the ϕ spatial variation and distribution can be explained by ensemble variations of granular tilts and surface slopes via a Smoluchowski smoothing model wherein local ϕ variations result from spatially varying densities of electric dipole moments, intrinsic to atomic steps, that locally modify ϕ. Atomic step-terrace structure is confirmed with scanning tunneling microscopy (STM) at several locations on our surfaces, and prior works showed STM evidence for atomic step dipoles at various metal surfaces. From our model, we find an atomic step edge dipole μ=0.12 D/edge atom, which is comparable to values reported in studies that utilized other methods and materials. Our results elucidate a connection between macroscopic ϕ and the nanostructure that may contribute to the spread of reported ϕ for Pt and other surfaces and may be useful toward more complete descriptions of polycrystalline metals in the models of field emission and other related vacuum electronics phenomena, e.g., arc initiation.

36 MATERIALS SCIENCE↗

DOE OSTI2019

NS1 DNA vaccination protects against Zika infection through T cell–mediated immunity in immunocompetent mice

The causal association of Zika virus (ZIKV) with microcephaly, congenital malformations in infants, and Guillain-Barré syndrome in adults highlights the need for effective vaccines. Thus far, efforts to develop ZIKV vaccines have focused on the viral envelope. ZIKV NS1 as a vaccine immunogen has not been fully explored, although it can circumvent the risk of antibody-dependent enhancement of ZIKV infection, associated with envelope antibodies. Here, we describe a novel DNA vaccine encoding a secreted ZIKV NS1, that confers rapid protection from systemic ZIKV infection in immunocompetent mice. We identify novel NS1 T cell epitopes in vivo and show that functional NS1-specific T cell responses are critical for protection against ZIKV infection. We demonstrate that vaccine-induced anti-NS1 antibodies fail to confer protection in the absence of a functional T cell response. This highlights the importance of using NS1 as a target for T cell–based ZIKV vaccines.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Identification of driver genes for critical forms of COVID-19 in a deeply phenotyped young patient cohort

The drivers of critical coronavirus disease 2019 (COVID-19) remain unknown. Given major confounding factors such as age and comorbidities, true mediators of this condition have remained elusive. We used a multi-omics analysis combined with artificial intelligence in a young patient cohort where major comorbidities were excluded at the onset. The cohort included 47 “critical” (in the intensive care unit under mechanical ventilation) and 25 “non-critical” (in a non-critical care ward) patients with COVID-19 and 22 healthy individuals. The analyses included whole-genome sequencing, whole-blood RNA sequencing, plasma and blood mononuclear cell proteomics, cytokine profiling, and high-throughput immunophenotyping. An ensemble of machine learning, deep learning, quantum annealing, and structural causal modeling were used. Patients with critical COVID-19 were characterized by exacerbated inflammation, perturbed lymphoid and myeloid compartments, increased coagulation, and viral cell biology. Among differentially expressed genes, we observed up-regulation of the metalloprotease ADAM9. This gene signature was validated in a second independent cohort of 81 critical and 73 recovered patients with COVID-19 and was further confirmed at the transcriptional and protein level and by proteolytic activity. Ex vivo ADAM9 inhibition decreased severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) uptake and replication in human lung epithelial cells. In conclusion, within a young, otherwise healthy, cohort of individuals with COVID-19, we provide the landscape of biological perturbations in vivo where a unique gene signature differentiated critical from non-critical patients. We further identified ADAM9 as a driver of disease severity and a candidate therapeutic target.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Laboratory evolution in Novosphingobium aromaticivorans enables rapid catabolism of a model lignin-derived aromatic dimer

Lignin contains a variety of interunit linkages, leading to a range of potential decomposition products that can be used as carbon and energy sources by microbes. β-O-4 linkages are the most common in native lignin, and associated catabolic pathways have been well characterized. However, the fate of the mono-aromatic intermediates that result from β-O-4 dimer cleavage has not been fully elucidated. Here, we used experimental evolution to identify mutant strains of Novosphingobium aromaticivorans with improved catabolism of a model aromatic dimer containing a β-O-4 linkage, guaiacylglycerol-β-guaiacyl ether (GGE). We identified several parallel causal mutations, including a single nucleotide polymorphism in the promoter of an uncharacterized gene that roughly doubled the growth yield with GGE. We characterized the associated enzyme and demonstrated that it oxidizes an intermediate in GGE catabolism, β-hydroxypropiovanillone, to vanilloyl acetaldehyde. Identification of this enzyme and its key role in GGE catabolism furthers our understanding of catabolic pathways for lignin-derived aromatic compounds.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Novel scalings of neutron star properties from analyzing dimensionless Tolman–Oppenheimer–Volkoff equations

The Tolman–Oppenheimer–Volkoff (TOV) equations govern the radial evolution of pressure and energy density in static neutron stars (NSs) in hydrodynamical equilibrium. Using the reduced pressure and energy density with respect to the NS central energy density, the original TOV equations can be recast into dimensionless forms. While the traditionally used integral approach for solving the original TOV equations require an input nuclear Equation of State (EOS), the dimensionless TOV equations can be anatomized by using the reduced pressure and energy density as polynomials of the reduced radial coordinate without using any input nuclear EOS. It has been shown in several of our recent works that interesting and novel perspectives about NS core EOS can be extracted directly from NS observables by using the latter approach. Our approach is based on intrinsic and perturbative analyses of the dimensionless (IPAD) TOV equations (IPAD-TOV). In this review article, we first discuss the length and energy density scales of NSs as well as the dimensionless TOV equations for scaled variables and their perturbative solutions near NS cores. We then review several new insights into NS physics gained from solving perturbatively the scaled TOV equations. Whenever appropriate, comparisons with the traditional approach from solving the original TOV equations will be made. In particular, we first show that the nonlinearity of the TOV equations basically excludes a linear EOS for dense matter in NS cores. We then show that perturbative analyses of the scaled TOV equations enable us to reveal novel scalings of the NS mass, radius and the compactness with certain combinations of the NS central pressure and energy density. Thus, observational data on either mass, radius or compactness can be used to constrain directly the core EOS of NS matter independent of the still very uncertain nuclear EOS models. As examples, the EOS of the densest visible matter in our Universe before the most massive neutron stars collapse into black holes (BHs) as well as the central EOS of a canonical or a 2.1 solar mass NS are extracted without using any nuclear EOS model. In addition, we show that causality in NSs sets an upper bound of about 0.374 for the ratio of pressure over energy density and correspondingly a lower limit for trace anomaly in supra-dense matter. We also demonstrate that the strong-field gravity plays a fundamental role in extruding a peak in the density/radius profile of the speed of sound squared (SSS) in massive NS cores independent of the nuclear EOS. Finally, some future perspectives of NS research using the new approach reviewed here by solving perturbatively the dimensionless TOV equations are outlined.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

On completeness and generalized symmetries in quantum field theory

We review a notion of completeness in QFT arising from the analysis of basic properties of the set of operator algebras attached to regions. In words, this completeness asserts that the physical observable algebras produced by local degrees of freedom are the maximal ones compatible with causality. We elaborate on equivalent statements to this completeness principle such as the non-existence of generalized symmetries and the uniqueness of the net of algebras. We clarify that for non-complete theories, the existence of generalized symmetries is unavoidable and further that they always come in dual pairs with precisely the same “size”. Moreover, the dual symmetries are always broken together, be it explicitly or effectively. Finally, we comment on several issues raised in recent literature, such as the relationship between completeness and modular invariance, dense sets of charges, and absence of generalized symmetries in the bulk of holographic theories.

Astronomy & Astrophysics↗

DOE OSTI2020

The vecro hypothesis

We consider three fundamental issues in quantum gravity: (a) the black hole information paradox (b) the unboundedness of entropy that can be stored inside a black hole horizon (c) the relation between the black hole horizon and the cosmological horizon. With help from the small corrections theorem, we convert each of these issues into a sharp conflict. We then argue that all three conflicts can be resolved by the following hypothesis: the vacuum wave functional of quantum gravity contains a “vecro” component made of virtual fluctuations of configurations of the same type that arise in the fuzzball structure of black hole microstates. Further, if we assume that causality holds to leading order in gently curved spacetime, then we must have such a vecro component in order to resolve the above conflicts. The term vecro stands for “Virtual Extended Compression-Resistant Object”, and characterizes the nature of the vacuum fluctuations that resolve the puzzles. It is interesting that puzzle (c) may relate the role of quantum gravity in black holes to observations in the sky.

Astronomy & Astrophysics↗

DOE OSTI2022

Are there echoes of gravitational waves?

In several approaches to evading the information paradox, the semiclassical black hole is replaced by an Exotic Compact Object (ECO). It has been conjectured that gravitational waves emitted by the merger of ECOs can reflect off the ECOs and produce a detectable ‘echo’. We argue that while a part of the wave can indeed reflect off the surface of an ECO, this reflected wave will get trapped by a new closed trapped surface produced by its own backreaction. Thus, no detectable signal of the echo will emerge to infinity. The only assumption in this analysis is that causality is maintained to leading order in gently curved spacetime. Thus, if echoes are actually detected, then we would face a profound change in our understanding of physics.

Astronomy & Astrophysics↗

DOE OSTI2025

Space cannot stretch too fast

Here, in this paper, we argue that black holes microstates leave an imprint on the gravitational vacuum through their virtual fluctuations. This imprint yields a power law fall off — rather than an exponential fall off — for the entanglement of planck scale fluctuations at different points. These entanglements generate an extra energy when space stretches too fast, since causality prevents a relaxation of these entanglements to their vacuum values. We obtain semiclassical dynamics for slow processes like star formation, but a radical departure from semiclassicality when a black hole horizon forms even though curvatures remain low everywhere. This resolution of the information puzzle also implies an extra energy source at the scale of the cosmological horizon, which may explain the mysteries of dark energy and the Hubble tension.

black holes↗

DOE OSTI2023

Experimental Study of TCP Throughput Profiles and Dynamics Over Dedicated Connections

A throughput profile expressed as a function of the round-trip times of a data transport infrastructure is a critical indicator of its level of optimization, particularly, over dedicated connections. We study the throughput profiles of eleven TCP versions using measurements collected over dedicated hardware-emulated connections with distances spanning round the earth. Among them, BBR has overall higher temporal variations and lower throughput profiles compared to five loss-based TCP versions, and the comparison with other TCP versions is mixed. Using the Poincare map regions of throughput time traces, we show that the richer dynamics of BBR are correlated with its lower throughput profiles. We present basic analytical results that indicate that higher temporal variations of TCP methods lead to lower throughput profiles, thereby establishing the underlying causality. Under external losses, BBR achieves sustained high throughput compared to others as the loss rate is increased. Overall, our results provide insights into the relationship between time dynamics and throughput profiles of TCP versions, in particular, show that limited time traces can be indicative of global properties of throughput profiles.

Rao, Nageswara↗

DOE OSTI2025

RingX: Scalable Parallel Attention for Long-Context Learning on HPC

The attention mechanism has become foundational for remarkable AI breakthroughs since the introduction of the Transformer, driving the demand for increasingly longer context to power frontier models such as large-scale reasoning language models and high-resolution image/video generators. However, its quadratic computational and memory complexities present substantial challenges. Current state-of-the-art parallel attention methods, such as ring attention, are widely adopted for long-context training but utilize a point-to-point communication strategy that fails to fully exploit the capabilities of modern HPC network architectures. In this work, we propose ringX, a scalable family of parallel attention methods optimized explicitly for HPC systems. By enhancing workload partitioning, refining communication patterns, and improving load balancing, ringX achieves up to 3.4 × speedup compared to conventional ring attention on the Frontier supercomputer. Optimized for both bi-directional and causal attention mechanisms, ringX demonstrates its effectiveness through training benchmarks of a Vision Transformer (ViT) on a climate dataset and a Generative Pre-Trained Transformer (GPT) model, Llama3 8B. Our method attains an end-to-end training speedup of approximately 1.5 × in both scenarios. To our knowledge, the achieved 38% model FLOPs utilization (MFU) for training Llama3 8B with a 1M-token sequence length on 4,096 GPUs represents one of the highest training efficiencies reported for long-context learning on HPC systems. Our code implementation is available at https://github.com/jqyin/ringX-attention.

Yin, Junqi [ORNL] (ORCID:0000000338435520)↗

DOE OSTI2020

fbWecCntrl

fbWecCntrl is set of MATLAB functions and scripts demonstrating a causal impedance matching approach to wave energy converter (WEC) control design. SAND2020-12219 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Coe, Ryan↗

DOE OSTI2021

CausalFS

SAND2021-4723 O CausalFS is a Python package that evaluates causal relationships between dataset features and the metric to be predicted. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Nguyen, Bernard↗

DOE OSTI2025

ciemss/pyciemss

Causal and probabilistic reasoning with continuous time dynamical systems

Witty, Sam [Sorbus AI]↗

DOE OSTI2020

Collagen Turnover in Relation to Risk Factors and Hemodynamics in Human Intracranial Aneurysms

Determinants for molecular and structural instability, that is, impending growth or rupture, of intracranial aneurysms (IAs) remain uncertain. To elucidate this, we endeavored to estimate the actual turnover rates of the main molecular constituent in human IA (collagen) on the basis of radiocarbon ( 14 C) birth dating in relation to IA hemodynamics. Collagen turnover rates in excised human IA samples were calculated using mathematical modeling of 14 C birth dating data of collagen in relation to risk factors and histological markers for collagen maturity/turnover in selected IA. Hemodynamics were simulated using image-based computational fluid dynamics. Correlation, logistic regression, and receiver operating characteristic analyses were performed. Collagen turnover rates were estimated in 46 IA (43 patients); computational fluid dynamics could be performed in 20 IA (20 patients). The mean collagen turnover rate (γ) constituted 126% (±1% error) per year. For patients with arterial hypertension, γ was greater than 2600% annually, whereas γ was distinctly lower with 32% (±1% error) per year for patients without risk factors, such as smoking and hypertension. There was a distinct association between histological presence of rather immature collagen in human IA and the presence of modifiable risk factors. Spatial-temporal averaged wall shear stress predicted rapid collagen turnover (odds ratio, 1.6 [95% CI, 1.0–2.7]). Receiver operating characteristic analysis demonstrated a good test accuracy (area under the curve, 0.798 [95% CI, 0.598–0.998]) for average wall shear stress with a threshold ≥4.9 Pa for rapid collagen turnover. Our data indicate that turnover rates and stability of collagen in human IA are strongly associated with the presence of modifiable risk factors and aneurysmal hemodynamics. Finally, these findings underline the importance of strict risk factor modification in patients with unruptured IA. Future should include more detailed risk factor data to establish a more causal understanding of hemodynamics and the rupture risk of individual IA.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Subseasonal Representation and Predictability of North American Weather Regimes Using Cluster Analysis

Abstract This study focuses on assessing the representation and predictability of North American weather regimes, which are persistent large-scale atmospheric patterns, in a set of initialized subseasonal reforecasts created using the Community Earth System Model, version 2 (CESM2). The k -means clustering was used to extract four key North American (10°–70°N, 150°–40°W) weather regimes within ERA5 reanalysis, which were used to interpret CESM2 subseasonal forecast performance. Results show that CESM2 can recreate the climatology of the four main North American weather regimes with skill but exhibits biases during later lead times with overoccurrence of the West Coast high regime and underoccurrence of the Greenland high and Alaskan ridge regimes. Overall, the West Coast high and Pacific trough regimes exhibited higher predictability within CESM2, partly related to El Niño. Despite biases, several reforecasts were skillful and exhibited high predictability during later lead times, which could be partly attributed to skillful representation of the atmosphere from the tropics to extratropics upstream of North America. The high predictability at the subseasonal time scale of these case-study examples was manifested as an “ensemble realignment,” in which most ensemble members agreed on a prediction despite ensemble trajectory dispersion during earlier lead times. Weather regimes were also shown to project distinct temperature and precipitation anomalies across North America that largely agree with observational products. This study further demonstrates that unsupervised learning methods can be used to uncover sources and limits of subseasonal predictability, along with systematic biases present in numerical prediction systems. Significance Statement North American weather regimes are large-scale atmospheric patterns that can persist for several days. Their skillful subseasonal (2 weeks or greater) prediction can provide valuable lead time to prepare for temperature and precipitation anomalies that can stress energy and water resources. The purpose of this study was to assess the climatological representation and subseasonal predictability of four key North American weather regimes using a research subseasonal prediction system and clustering analysis. We found that the Pacific trough and West Coast high regimes exhibited higher predictability than other regimes and that skillful representation of conditions across the tropics and extratropics can increase predictability during later lead times. Future work will quantify causal pathways associated with high predictability.

58 GEOSCIENCES↗

DOE OSTI2019

Interaction of the Westerlies with the Tibetan Plateau in Determining the Mei-Yu Termination

This article explores how the termination of the mei-yu is dynamically linked to the westerlies impinging on the Tibetan Plateau. It is found that the mei-yu stage terminates when the maximum upper-tropospheric westerlies shift beyond the northern edge of the plateau, around 40°N. This termination is accompanied by the disappearance of tropospheric northerlies over northeastern China. The link between the transit of the jet axis across the northern edge of the plateau, the disappearance of northerlies, and termination of the mei-yu holds on a range of time scales from interannual through seasonal and pentad. Diagnostic analysis indicates that the weakening of the meridional moisture contrast and meridional wind convergence, mainly resulting from the disappearance of northerlies, causes the demise of the mei-yu front. The authors propose that the westerlies migrating north of the plateau and consequent weakening of the extratropical northerlies triggers the mei-yu termination. Model simulations are employed to test the causality between the jet and the orographic downstream northerlies by repositioning the northern edge of the plateau. As the plateau edge extends northward, orographic forcing on the westerlies strengthens, leading to persistent strong downstream northerlies and a prolonged mei-yu. Idealized simulations with a dry dynamical core further demonstrate the dynamical link between the weakening of orographically forced downstream northerlies with the positioning of the jet from south to north of the plateau. Changes in the magnitude of orographically forced stationary waves are proposed to explain why the downstream northerlies disappear when the jet axis migrates beyond the northern edge of the plateau.

54 ENVIRONMENTAL SCIENCES↗