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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Fault-tolerant resource comparison of qudit and qubit encodings for diagonal quadratic operators

Finite local Hilbert-space truncations arise naturally in quantum simulations of lattice field theories and motivate qudit encodings, but their fault-tolerant advantage over qubit encodings remains unclear. We compare the non-Clifford cost of implementing quadratic diagonal evolutions, exemplified by 𝑈 = 𝑒$^{−𝑖⁢𝑡⁢𝜙^2_𝑥}$ in a uniform field-amplitude discretization of a real scalar field, using either one logical 𝑑-level qudit or 𝑛 𝑏 = ⌈log 2⁡ 𝑑⌉ logical qubits. We analyze two standard settings: product-formula simulation and linear combination of unitaries (LCU) per block encoding, taking the resource metric to be the number of non-Clifford gates after synthesis into a discrete logical gate set. Because tight synthesis bounds for general single-qudit rotations are not known, we express the qudit constructions in terms of embedded two-level SU⁡(2) rotations and derive explicit finite-𝑑 break-even conditions for their synthesis cost; these serve as compiler targets for when qudit encodings can outperform the qubit baseline. Within the constructive models studied here, product-formula implementations would require an exponentially stronger per-primitive synthesis advantage for qudits to win asymptotically, while in the LCU setting the qubit encoding is asymptotically cheaper in 𝑑. Nevertheless, the finite-𝑑 threshold analysis identifies low-dimensional regions in which qudits can yield meaningful constant-factor savings, particularly for LCU-based implementations. As a secondary analysis of the LCU construction, we use an idealized negligible-overhead qubit-qudit code-switching model to give an absolute 𝑇-count comparison and reinterpret the savings as an allowable per-switch overhead budget.

Godwood, Samuel [Univ. of Liverpool (United Kingdo↗
DOE OSTI2025

Anisotropic light-tailored RKKY interaction in two-dimensional 𝑑-wave altermagnets

Altermagnets are known in spintronics for their intrinsic spin-splitting and unconventional magnetic responses, particularly to magnetic impurities. However, effectively controlling the magnetic exchange interactions in altermagnets is challenging for practical applications. Here, in this work, we propose using circularly polarized light to tune the Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction in two-dimensional 𝑑-wave altermagnets. Using the real-space retarded Green's functions approach, our results show that while the Heisenberg and Ising exchanges dominate, a notable Dzyaloshinskii–Moriya (DM) interaction also plays a key role. Furthermore, the inherent strength of altermagnetism imprints chirp-like signatures into the magnetic responses, which can be dynamically tuned via light. We mainly demonstrate that gate-induced Rashba spin-orbit coupling is essential in response to light—light selectively and anisotropically adjusts the DM interaction without affecting the other exchanges. Our findings further indicate that rotating the altermagnet by 45° relative to the light's polarization direction generates a Dirac-like dispersion and different DM interactions. We finally extract critical thresholds where light reverses DM interactions along one axis or balances both in-plane components. The anisotropic light-driven control of RKKY interactions in altermagnets not only highlights their unique properties but also opens new avenues for engineering tailored magnetic characteristics in spintronic applications.

altermagnetism↗
DOE OSTI2024

Generalization error guaranteed auto-encoder-based nonlinear model reduction for operator learning

Many physical processes in science and engineering are naturally represented by operators between infinite-dimensional function spaces. The problem of operator learning, in this context, seeks to extract these physical processes from empirical data, which is challenging due to the infinite or high dimensionality of data. An integral component in addressing this challenge is model reduction, which reduces both the data dimensionality and problem size. In this paper, we utilize low-dimensional nonlinear structures in model reduction by investigating Auto-Encoder-based Neural Network (AENet). AENet first learns the latent variables of the input data and then learns the transformation from these latent variables to corresponding output data. Our numerical experiments validate the ability of AENet to accurately learn the solution operator of nonlinear partial differential equations. Furthermore, we establish a mathematical and statistical estimation theory that analyzes the generalization error of AENet. Finally, our theoretical framework shows that the sample complexity of training AENet is intricately tied to the intrinsic dimension of the modeled process, while also demonstrating the robustness of AENet to noise.

Auto-encoder↗
DOE OSTI2022

A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems

Constructing surrogate models for uncertainty quantification (UQ) on complex partial differential equations (PDEs) having inherently high-dimensional O(10 n ), n ≥ 2, stochastic inputs (e.g., forcing terms, boundary conditions, initial conditions) poses tremendous challenges. The “curse of dimensionality” can be addressed with suitable unsupervised learning techniques used as a pre-processing tool to encode inputs onto lower-dimensional subspaces while retaining its structural information and meaningful properties. In this work, we review and investigate thirteen dimension reduction methods including linear and nonlinear, spectral, blind source separation, convex and non-convex methods and utilize the resulting embeddings to construct a mapping to quantities of interest via polynomial chaos expansions (PCE). Here, we refer to the general proposed approach as manifold PCE (m-PCE), where manifold corresponds to the latent space resulting from any of the studied dimension reduction methods. To investigate the capabilities and limitations of these methods we conduct numerical tests for three physics-based systems (treated as black-boxes) having high-dimensional stochastic inputs of varying complexity modeled as both Gaussian and non-Gaussian random fields to investigate the effect of the intrinsic dimensionality of input data. We demonstrate both the advantages and limitations of the unsupervised learning methods and we conclude that a suitable m-PCE model provides a cost-effective approach compared to alternative algorithms proposed in the literature, including recently proposed expensive deep neural network-based surrogates and can be readily applied for high-dimensional UQ in stochastic PDEs.

42 ENGINEERING↗
DOE OSTI2024

Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification

In this work we introduce a manifold learning-based surrogate modeling framework for uncertainty quantification in high-dimensional stochastic systems. Our first goal is to perform data mining on the available simulation data to identify a set of low-dimensional (latent) descriptors that efficiently parameterize the response of the high-dimensional computational model. To this end, we employ Principal Geodesic Analysis on the Grassmann manifold of the response to identify a set of disjoint principal geodesic submanifolds, of possibly different dimension, that captures the variation in the data. Since operations on the Grassmann require the data to be concentrated, we propose an adaptive algorithm based on Riemannian K-means and the minimization of the sample Fréchet variance on the Grassmann manifold to identify “local” principal geodesic submanifolds that represent different system behavior across the parameter space. Polynomial chaos expansion is then used to construct a mapping between the random input parameters and the projection of the response on these local principal geodesic submanifolds. Here, the method is demonstrated on four test cases, a toy-example that involves points on a hypersphere, a Lotka-Volterra dynamical system, a continuous-flow stirred-tank chemical reactor system, and a two-dimensional Rayleigh-Bénard convection problem.

42 ENGINEERING↗
DOE OSTI2025

Orbital-Selective Instabilities and Spin Fluctuations at the Verge of Superconductivity in Interlayer-Expanded Iron Selenide

Understanding electron correlation-driven instabilities and their coupling to structural phases is essential for deciphering multiorbital pairing in unconventional superconductors. We investigate Li x (C 5 H 5 N) y Fe 2 Se 2 (x ∼ 0.6; y ∼ 0.7−0.9), a tetragonal β-FeSe intercalate with a superconducting transition temperature (T c = 39 K) closely tied to an expanded Fe-layer spacing (∼11.4 Å). High-resolution synchrotron Xray diffraction and core-level absorption spectroscopy reveal subtle lattice distortions on cooling without a symmetry-breaking transition. Instead, the material exhibits negative thermal expansion (NTE) in the two-dimensional Fe network below T S ∼ 70 K, and stiffening of local Se−Fe−Se bond dynamics near T c . The spatially incoherent rearrangement of FeSe 4 tetrahedra and the site-local fluctuations, signal reduced electron correlations compared to those of parent β-FeSe (T c = 8 K). Complementary X-ray emission spectroscopy, a fast local probe of Fe 3d valence states, detects persistent local Fe spin moments below T S , unlike quenching in related systems. These findings indicate that decoupling of Fe planes leads to an electronically driven lattice instability. The latter emerges as NTE induced from weak, orbital-selective localization of in-plane Fe 3d states rather than conventional transverse vibrations. Governed by Hund’s coupling, this selectivity permits coexistence of local spin fluctuations with itinerant d-electrons critical for enhancing T c . These results suggest that intercalation-driven d-orbital differentiation moderates electron correlations, providing a pathway to optimize the superconductivity in low-dimensional quantum materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Formation of a Secondary Phase in Thermally Evaporated MAPbI 3 and Its Effects on Solar Cell Performance

Thermal evaporation is a promising deposition technique to scale up perovskite solar cells (PSCs) to large areas, but the lack of understanding of the mechanisms that lead to high-quality evaporated methylammonium lead triiodide (MAPbI 3 ) films gives rise to devices with efficiencies lower than those obtained by spin coating. Here this work investigates the crystalline properties of MAPbI 3 deposited by the thermal coevaporation of PbI 2 and MAI, where the MAI evaporation rate is controlled by setting different temperatures for the MAI source and the PbI 2 deposition rate is controlled with a quartz crystal microbalance (QCM). Using grazing incident wide-angle X-ray scattering (GIWAXS) and X-ray diffraction (XRD), we identify the formation of a secondary orthorhombic phase (with a Pnma space group) that appears at MAI source temperatures below 155 °C. With synchrotron-based X-ray fluorescence (XRF) microscopy, we show that the changes in crystalline phases are not necessarily due to changes in stoichiometry. The films show a stochiometric composition when the MAI source is heated between 140 to 155 °C, and the samples become slightly MAI rich at 165 °C. Increasing the MAI temperature beyond 165 °C introduces an excess of MAI in the film, which promotes the formation of films with low crystallinity that contain low-dimensional perovskites. When they are incorporated in solar cells, the films deposited at 165 °C result in the champion power conversion efficiency, although the presence of a small amount of low-dimensional perovskite may lead to a lower open-circuit voltage. We hypothesize that the formation of secondary phases in evaporated films limits the performance of PSCs and that their formation can be suppressed by controlling the MAI source temperature, bringing the film toward a phase-pure tetragonal structure. Control of the phases during perovskite evaporation is therefore crucial to obtain high-performance solar cells.

36 MATERIALS SCIENCE↗
DOE OSTI2022

“Breathing” organic cation to stabilize multiple structures in low-dimensional Ge-, Sn-, and Pb-based hybrid iodide perovskites

Low-dimensional hybrid inorganic–organic perovskites are excellent candidates for stable optoelectronic devices. The dimensionality of these perovskites depends largely on the organic and inorganic compositions, as well as the synthetic conditions. We report five new hybrid iodides, (ETU) 4 Ge 5 I 18 , (ETU)GeI 4 , (ETU)SnI 4 , (ETU)PbI 4 , and (ETU) 3 Pb 2 I 10 using only one type of organic cation, namely, S-(2-aminoethyl)isothiouronium (ETU). (ETU)GeI 4 and (ETU)SnI 4 belong to the (110)-oriented structure-type with “3 × 3” sawtooth corrugated layers and crystallize in a structure with the orthorhombic space group Pbca. (ETU) 4 Ge 5 I 18 crystallizes in a structure with the triclinic space group P$\bar{1}$with combining macron], featuring a 2D layered structure with combinations of corner, edge, and face-sharing [GeI 6 ] octahedra. For the Pb-based series, (ETU)PbI 4 has the conventional (100) – oriented 2D type whereas (ETU) 3 Pb 2 I 10 has a unique 0D structure. Remarkably, the unstable 2D orange-phase (ETU)PbI 4 transforms to a stable 0D yellow phase (ETU) 3 Pb 2 I 10 , accompanied by the reduction of the C–S–C angle of the organic cation ETU. The optical band gaps are largely regulated by the diverse types of structure and are in the range of 1.8 eV to 2.8 eV. (ETU)SnI 4 is the only material showing notable photoluminescence at room-temperature. Our work showcases the flexibility of the organic cation in determining the structural dimensionality and provides a new strategy in generating new hybrid materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Dark Energy Survey Year 3 results: Simulation-based 𝑤CDM inference from weak lensing and galaxy clustering maps with deep learning: Analysis design

Data-driven approaches using deep learning are emerging as powerful techniques to extract non-Gaussian information from cosmological large-scale structure. Here, this work presents the first simulation-based inference (SBI) pipeline that combines weak lensing and galaxy clustering maps in a realistic Dark Energy Survey Year 3 (DES Y3) configuration and serves as preparation for a forthcoming analysis of the survey data. We develop a scalable forward model based on the CosmoGridV1 suite of N-body simulations to generate over one million self-consistent mock realizations of DES Y3 at the map level. Leveraging this large dataset, we train deep graph convolutional neural networks on the full survey footprint in spherical geometry to learn low-dimensional features that approximately maximize mutual information with target parameters. These learned compressions enable neural density estimation of the implicit likelihood via normalizing flows in a ten-dimensional parameter space spanning cosmological 𝑤CDM, intrinsic alignment, and linear galaxy bias parameters, while marginalizing over baryonic, photometric redshift, and shear bias nuisances. To ensure robustness, we extensively validate our inference pipeline using synthetic observations derived from both systematic contaminations in our forward model and independent Buzzard galaxy catalogs. Our forecasts yield significant improvements in cosmological parameter constraints, achieving 2−3× higher figures of merit in the 𝛺 𝑚 − 𝑆 8 plane relative to our implementation of baseline two-point statistics and effectively breaking parameter degeneracies through probe combination. These results demonstrate the potential of SBI analyses powered by deep learning for upcoming Stage-IV wide-field imaging surveys.

Thomsen, A. [Zurich, ETH] (ORCID:0000000203099021)↗
DOE OSTI2024

Localized modes in the IR phase of QCD

Infrared (IR) dimension function d IR ( λ ) characterizes the space effectively utilized by QCD quarks at Dirac scale λ , and indirectly the space occupied by glue fields. It was proposed that its nonanalytic behavior in thermal reflects the separation of QCD system into an IR component and an independent bulk. Here we study the “plateau modes” in the IR component, whose dimensional properties were puzzling. Indeed, in the recent scenario of transition to IR phase, this low-dimensional plateau connects the Anderson-like mobility edge λ IR = 0 in Dirac spectrum with mobility edges ± λ A . For this structure to be truly Anderson-like, plateau modes have to be exponentially localized, implying that both the effective distances L eff ∝ L γ and the effective volumes V eff ∝ L d IR in these modes grow slower than any positive power of IR cutoff L . Although γ = 0 was confirmed in the plateau, it was found that d IR ≈ 1 . Here we apply the recently proposed technique to the problem. We conclude that a plateau mode of pure-glue QCD at UV cutoff a = 0.085 fm occupies a subvolume of IR dimension zero with probability at least 0.9999, substantiating this aspect of metal-to-critical scenario to a respective degree. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2023

Quasi-one-dimensional Pb 5 Re 3 O 15 : A 5 d realization of the Heisenberg antiferromagnetic spin-1/2 chain

Quasi-one-dimensional (1D) magnetic compounds connect the exact solutions of low-dimensional magnetic geometries, which promise quantum spin liquid behavior and exotic quasiparticles, with real-world materials, in which competing magnetic interactions affect their implementation in quantum information science. Here, the structural determination and quasi-1D magnetic behavior of a previously unreported compound, Pb 5 ⁢Re 3 ⁡O 15 , is presented. Like the anisotropic triangular A 3 ⁢ReO 5 ⁢Cl 2 (A = Ba, Sr, Ca) materials, Pb 5 ⁢Re 3 ⁡O 15 contains [ReO 5 ] square pyramids inserted into anion-centered quasi-two-dimensional layers and hosts spin-1/2 moments on the Re 6+ ions. Pb 5 ⁢Re 3 ⁡O 15 , however, has a more ideal quasi-1D geometry than the A 3 ⁢ReO 5 ⁢Cl 2 materials, with larger interchain distances and interlayer spacing. Quasi-1D magnetic behavior in Pb 5 ⁢Re 3 ⁡O 15 is confirmed by fitting the temperature-dependent magnetic susceptibility with the Bonner-Fisher model for a spin-1/2 antiferromagnetically coupled chain, yielding an intrachain coupling constant of |J|/k B =54.5K. Pb 5 ⁢Re 3 ⁡O 15 is highly insulating at room temperature, and heat capacity data below 10 K reveal a linear-T contribution that suggests the presence of low-temperature spinon excitations. Finally, with a lack of three-dimensional ordering down to at least 0.6 K, Pb 5 ⁢Re 3 ⁡O 15 is proposed as a model system for studying the quantum magnetism of quasi-1D Heisenberg chains in a real-world 5d 1 antiferromagnetic material.

36 MATERIALS SCIENCE↗
DOE OSTI2024

La 4 Co 4 X ( X = Pb , Bi , Sb ) : A demonstration of antagonistic pairs as a route to quasi-low-dimensional ternary compounds

We outline how pairs of strongly immiscible elements, referred to here as antagonistic pairs, can be used to synthesize ternary compounds with low or quasi-reduced-dimensional motifs intrinsically built into their crystal structures. By identifying third elements that are mutually compatible with a given antagonistic pair, ternary compounds can be formed in which the third element segregates the immiscible atoms into spatially separated substructures. Quasi-low-dimensional structural units, such as sheets, chains, or clusters are a natural consequence of the immiscible atoms seeking to avoid close contact in the solid state. Further, as proof of principle, we present the discovery, crystal growth, and basic physical properties of La 4 ⁢Co 4 ⁢$\mathrm{X}$ (X = Pb, Bi, Sb), a family of intermetallic compounds based on the antagonistic pairs Co-Pb and Co-Bi. La 4 ⁢Co 4 ⁢$\mathrm{X}$ adopts an orthorhombic crystal structure (space group Pbam) containing quasi-two-dimensional Co slabs and La-X polyhedra that stack in an alternating manner along the α axis. Consistent with our proposal, the La atoms separate the Co and X substructures, ensuring there are no direct contacts between the members of the immiscible (antagonistic) pair. Within the Co slabs, the atoms occupy the vertices of corner sharing tetrahedra and triangles, and this bonding motif produces narrow electronic bands near the Fermi level that favor magnetism. The Co is moment bearing in each La 4 ⁢Co 4 $\mathrm{X}$ compound studied, and we show that whereas La 4 ⁢Co 4 ⁢Pb behaves as a three-dimensional antiferromagnet with T N =220K, La 4 ⁢Co 4 ⁢Bi and La 4⁢ Co 4 ⁢Sb have behavior consistent with low-dimensional magnetic coupling and ordering, with T N =153K and 143 K, respectively. In addition to the Pb-, Bi-, and Sb-based La 4 ⁢Co 4 ⁢$\mathrm{X}$ compounds, we also were likely able to produce an analogous La 4 ⁢Co 4 ⁢Sn in polycrystalline form, although we were unable to isolate single crystals. We anticipate that identifying and using mutually compatible third elements together with an antagonistic pair represents a generalizable design principle for discovering new materials and new structure types containing low-dimensional substructures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Data-Driven Compression of Electron-Phonon Interactions

First-principles calculations of electron interactions in materials have seen rapid progress in recent years, with electron-phonon ( e − ph ) interactions being a prime example. However, these techniques use large matrices encoding the interactions on dense momentum grids, which reduces computational efficiency and obscures interpretability. For e − ph interactions, existing interpolation techniques leverage locality in real space, but the high dimensionality of the data remains a bottleneck to balance cost and accuracy. Here we show an efficient way to compress e − ph interactions based on singular value decomposition (SVD), a widely used matrix and image compression technique. Leveraging (un)constrained SVD methods, we accurately predict material properties related to e − ph interactions—including charge mobility, spin relaxation times, band renormalization, and superconducting critical temperature—while using only a small fraction (1%–2%) of the interaction data. These findings unveil the hidden low-dimensional nature of e − ph interactions. Furthermore, they accelerate state-of-the-art first-principles e − ph calculations by about 2 orders of magnitude without sacrificing accuracy. Our Pareto-optimal parametrization of e − ph interactions can be readily generalized to electron-electron and electron-defect interactions, as well as to other couplings, advancing quantitative studies of condensed matter. Published by the American Physical Society 2024

Physics↗
DOE OSTI2026

Characterizing skyrmion flow phases with principal component analysis

Principal component analysis (PCA) is a powerful method that can identify patterns in large, complex data sets by constructing low-dimensional order parameters from higher-dimensional feature vectors. There are increasing efforts to use space-and-time-dependent PCA to detect transitions in nonequilibrium systems that are difficult to characterize with equilibrium methods. Here, we demonstrate that feature vectors incorporating the position and velocity information of driven skyrmions moving through random disorder permit PCA to resolve different types of disordered skyrmion motion as a function of driving force and the ratio of the Magnus force to the dissipation. Since the Magnus force creates gyroscopic motion and a finite Hall angle, skyrmions can exhibit a greater range of flow phases than what is observed in overdamped driven systems with quenched disorder. We show that in addition to identifying previously known skyrmion flow phases, PCA detects several additional phases, including different types of channel flow, moving fluids, and partially ordered states. Guided by the PCA analysis, we further characterize the disordered flow phases to elucidate the different microscopic dynamics and show that the changes in the PCA-derived order parameters can be connected to features in bulk transport measures, including the transverse and longitudinal velocity-force curves, differential conductivity, topological defect density, and changes in the skyrmion Hall angle as a function of drive. We discuss how asymmetric feature vectors can be used to improve the resolution of the PCA analysis, and how this technique can be extended to find disordered phases in other nonequilibrium systems with time-dependent dynamics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Learning Canonical Embeddings for Unsupervised Shape Correspondence With Locally Linear Transformations

We present a new approach to unsupervised shape correspondence learning between pairs of point clouds. We make the first attempt to adapt the classical locally linear embedding algorithm (LLE)-originally designed for nonlinear dimensionality reduction-for shape correspondence. The key idea is to find dense correspondences between shapes by first obtaining high-dimensional neighborhood-preserving embeddings of low-dimensional point clouds and subsequently aligning the source and target embeddings using locally linear transformations. We demonstrate that learning the embedding using a new LLE-inspired point cloud reconstruction objective results in accurate shape correspondences. More specifically, the approach comprises an end-to-end learnable framework of extracting high-dimensional neighborhood-preserving embeddings, estimating locally linear transformations in the embedding space, and reconstructing shapes via divergence measure-based alignment of probability density functions built over reconstructed and target shapes. Our approach enforces embeddings of shapes in correspondence to lie in the same universal/canonical embedding space, which eventually helps regularize the learning process and leads to a simple nearest neighbors approach between shape embeddings for finding reliable correspondences. Comprehensive experiments show that the new method makes noticeable improvements over state-of-the-art approaches on standard shape correspondence benchmark datasets covering both human and nonhuman shapes.

deformation↗
DOE OSTI2025

Data-Driven Closures and Assimilation for Stiff Multiscale Random Dynamics

Here, we introduce a data-driven and physics-informed framework for propagating uncertainty in stiff, multiscale random ordinary differential equations (RODEs) driven by correlated (colored) noise. Unlike systems subjected to Gaussian white noise, a deterministic equation for the joint probability density function (PDF) of RODE state variables does not exist in closed form. Moreover, such an equation would require as many phase-space variables as there are states in the RODE system. To alleviate this curse of dimensionality, we instead derive exact, albeit unclosed, reduced-order PDF (RoPDF) equations for low-dimensional observables/quantities of interest. The unclosed terms take the form of state-dependent conditional expectations, which are directly estimated from data at sparse observation times. However, for systems exhibiting stiff, multiscale dynamics, data sparsity introduces regression discrepancies that compound during RoPDF evolution. This is overcome by introducing a kinetic-like defect term to the RoPDF equation, which is learned by assimilating in sparse, low-fidelity RoPDF estimates. Two assimilation methods are considered, namely nudging and deep neural networks, which are successfully tested against Monte Carlo simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Scalable Bayesian Physics-Informed Kolmogorov-Arnold Networks

Uncertainty quantification (UQ) plays a pivotal role in scientific machine learning, especially when surrogate models are used to approximate complex systems. Although multilayer perceptions (MLPs) are commonly employed as surrogates, they often suffer from overfitting due to their large number of parameters. Kolmogorov-Arnold networks (KANs) offer an alternative solution with fewer parameters. However, gradient-based inference methods, such as Hamiltonian Monte Carlo (HMC), may result in computational inefficiency when applied to KANs, especially for large-scale datasets, due to the high cost of back-propagation. To address these challenges, we propose a novel approach, combining the dropout Tikhonov ensemble Kalman inversion (DTEKI) with Chebyshev KANs. This gradient-free method effectively mitigates overfitting and enhances numerical stability. In addition, we incorporate the active subspace method to reduce the parameter-space dimensionality, allowing us to improve the accuracy of predictions and obtain more reliable uncertainty estimates. Extensive experiments demonstrate the efficacy of our approach in various test cases, including scenarios with large datasets and high noise levels. Our results show that the new method achieves comparable or better accuracy, much higher efficiency as well as stability compared to HMC, in addition to scalability. Moreover, by leveraging the low-dimensional parameter subspace, our method preserves prediction accuracy while substantially reducing further the computational cost.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Manifold Learning-Based Polynomial Chaos Expansions for High-Dimensional Surrogate Models

In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding of a set of high-dimensional data representing quantities of interest of the computational or analytical model. For this purpose, we employ Grassmannian diffusion maps, a two-step nonlinear dimension reduction technique which allows us to reduce the dimensionality of the data and identify meaningful geometric descriptions in a parsimonious and inexpensive manner. Polynomial chaos expansion is then used to construct a mapping between the stochastic input parameters and the diffusion coordinates of the reduced space. An adaptive clustering technique is proposed to identify an optimal number of clusters of points in the latent space. The similarity of points allows us to construct a number of geometric harmonic emulators which are finally utilized as a set of inexpensive pretrained models to perform an inverse map of realizations of latent features to the ambient space and thus perform accurate out-of-sample predictions. Thus, the proposed method acts as an encoder-decoder system which is able to automatically handle very high-dimensional data while simultaneously operating successfully in the small-data regime. The method is demonstrated on two benchmark problems and on a system of advection-diffusion-reaction equations which model a first-order chemical reaction between two species. In all test cases, the proposed method is able to achieve highly accurate approximations which ultimately lead to the significant acceleration of UQ tasks.

42 ENGINEERING↗