A geometry-aligned bi-fidelity surrogate maps low- and high-fidelity wildfire solutions to a common domain for improved reduced-basis reconstruction, lower error near fronts, and practical uncertainty quantification.
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cs.CE 6 math.NA 6 cs.LG 2 cond-mat.mtrl-sci 1 cs.GR 1 cs.SE 1 math.ST 1 physics.app-ph 1 quant-ph 1verdicts
UNVERDICTED 20representative citing papers
QPCA-EnDCF is a deterministic ensemble data assimilation method that replaces stochastic observation perturbations with a spectrally regularized rank-κ update on whitened residuals, yielding better spread-skill and rank-histogram reliability than stochastic EnKF on Lorenz-96 in undersampled regimes.
A multilinear operator learned on PCA coefficients maps time-since-ignition inputs to smoke outputs, matching Monte Carlo accuracy with half the model calls and outperforming prior classifiers on holdout data.
A weighted FOSLS formulation for deep neural networks solves transmission problems robustly, with proofs that the loss aligns with the energy norm independently of material contrast and shows passive variance reduction.
Phase-field peridynamics degrades bond energies continuously via a bond phase-field parameter while using kinematic degradation to preserve nonlocal deformation gradient accuracy, with an analytically derived normalization constant for thermodynamic consistency.
Input-convex neural networks in elementary polynomials of signed singular values provably approximate any frame-indifferent isotropic polyconvex hyperelastic energy.
A thermodynamics-constrained ML framework learns robust, consistent constitutive models for inelastic materials from macroscopic stress-strain data and generalizes to unseen paths.
NSPOD is a multigrid-like preconditioner using DeepONet-learned POD subspaces that dramatically cuts Krylov solver iterations for solid mechanics PDEs on unstructured CAD geometries, outperforming algebraic multigrid.
A variational neural network using Kolosov-Muskhelishvili potentials solves 2D linear elasticity and fracture problems by minimizing total potential energy and embedding crack discontinuities into the ansatz, yielding higher accuracy and faster convergence than standard physics-informed networks.
EMSL groups material points into clusters, samples a reference strain per cluster once per increment, and computes a linearised stress estimate from the reference tangent and POD strain modes, yielding an affine reduced system that requires no iterations online and Pareto-dominates prior strain-cubc
The proximal Galerkin method reformulates phase-field fracture constraints into saddle-point problems to enforce physical bounds and irreversibility for static and dynamic cases.
Non-conformal immersed and union-based isogeometric methods with boundary-conformal quadrature reduce patch count and preprocessing for magnetostatics while union variants maintain accuracy on benchmarks.
A neural network with periodic activations parameterizes thin-shell mid-surfaces so that network weights can be optimized to minimize structural compliance subject to a volume limit.
A novel high-order stabilization-free virtual element method is developed for general second-order elliptic eigenvalue problems, with optimal a priori error estimates for eigenspaces and eigenvalues, validated on various polygonal meshes.
A decoupled kernel-only stabilization for finite-strain VEM hyperelasticity is introduced that scales deviatoric terms by shear modulus with geometry weights and volumetric terms independently by bulk modulus, with uniform stability proven under polygon regularity.
CBMD decomposes non-Hermitian operators via contour residues to enable optimal-query quantum simulation of first-order dynamics and special functions such as Bessel and Airy evolutions without requiring diagonalizability.
AMORE develops an adaptive multi-output DeepONet with custom losses, partition-of-unity trunk, and invertible/softmax mass-fraction maps to surrogate stiff kinetics on syngas (12 states) and GRI-Mech (24 states).
A robust containment query for collections of trimmed NURBS surfaces that computes generalized winding numbers directly via adaptive quadrature on solid angle boundary integrals without surface discretization.
High-fidelity multiphysics simulations of laser powder bed fusion melt pools match 2025 NIST experimental data across depth, width, bead height, overlap, and area metrics for varied powder heights and geometries.
Continuous integration workflows automate benchmarking of numerical cut-cell quadrature across scientific software packages.
citing papers explorer
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A geometry-aligned multi-fidelity framework for uncertainty quantification of wildfire spread
A geometry-aligned bi-fidelity surrogate maps low- and high-fidelity wildfire solutions to a common domain for improved reduced-basis reconstruction, lower error near fronts, and practical uncertainty quantification.
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A Data-Consistent Approach to Ensemble Filtering
QPCA-EnDCF is a deterministic ensemble data assimilation method that replaces stochastic observation perturbations with a spectrally regularized rank-κ update on whitened residuals, yielding better spread-skill and rank-histogram reliability than stochastic EnKF on Lorenz-96 in undersampled regimes.
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Enabling Real-Time Training of a Wildfire-to-Smoke Map with Multilinear Operators
A multilinear operator learned on PCA coefficients maps time-since-ignition inputs to smoke outputs, matching Monte Carlo accuracy with half the model calls and outperforming prior classifiers on holdout data.
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Robust Deep FOSLS for Transmission Problems
A weighted FOSLS formulation for deep neural networks solves transmission problems robustly, with proofs that the loss aligns with the energy norm independently of material contrast and shows passive variance reduction.
-
Phase-Field Peridynamics
Phase-field peridynamics degrades bond energies continuously via a bond phase-field parameter while using kinematic degradation to preserve nonlocal deformation gradient accuracy, with an analytically derived normalization constant for thermodynamic consistency.
-
Input convex neural networks: universal approximation theorem and implementation for isotropic polyconvex hyperelastic energies
Input-convex neural networks in elementary polynomials of signed singular values provably approximate any frame-indifferent isotropic polyconvex hyperelastic energy.
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Learning inelastic constitutive models from stress-strain data under hard thermodynamic constraints
A thermodynamics-constrained ML framework learns robust, consistent constitutive models for inelastic materials from macroscopic stress-strain data and generalizes to unseen paths.
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NSPOD: Accelerating Krylov solvers via DeepONet-learned POD subspaces
NSPOD is a multigrid-like preconditioner using DeepONet-learned POD subspaces that dramatically cuts Krylov solver iterations for solid mechanics PDEs on unstructured CAD geometries, outperforming algebraic multigrid.
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A Variational Kolosov--Muskhelishvili Network for Elasticity and Fracture
A variational neural network using Kolosov-Muskhelishvili potentials solves 2D linear elasticity and fracture problems by minimizing total potential energy and embedding crack discontinuities into the ansatz, yielding higher accuracy and faster convergence than standard physics-informed networks.
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Empirical Material Sampling and Linearisation -- A Simple and Efficient Strain-Space Model Order Reduction Approach for Computational Homogenisation in Large-Deformation Hyperelasticity
EMSL groups material points into clusters, samples a reference strain per cluster once per increment, and computes a linearised stress estimate from the reference tangent and POD strain modes, yielding an affine reduced system that requires no iterations online and Pareto-dominates prior strain-cubc
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Proximal Galerkin for Phase Field Fracture
The proximal Galerkin method reformulates phase-field fracture constraints into saddle-point problems to enforce physical bounds and irreversibility for static and dynamic cases.
-
Immersed boundary-conformal isogeometric methods for magnetostatics
Non-conformal immersed and union-based isogeometric methods with boundary-conformal quadrature reduce patch count and preprocessing for magnetostatics while union variants maintain accuracy on benchmarks.
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Neural parametric representations for thin-shell shape optimisation
A neural network with periodic activations parameterizes thin-shell mid-surfaces so that network weights can be optimized to minimize structural compliance subject to a volume limit.
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A high order stabilization-free virtual element method for general second-order elliptic eigenvalue problem
A novel high-order stabilization-free virtual element method is developed for general second-order elliptic eigenvalue problems, with optimal a priori error estimates for eigenspaces and eigenvalues, validated on various polygonal meshes.
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An Investigation of Stabilization Scaling in Finite-Strain Virtual Element Methods for Hyperelasticity
A decoupled kernel-only stabilization for finite-strain VEM hyperelasticity is introduced that scales deviatoric terms by shear modulus with geometry weights and volumetric terms independently by bulk modulus, with uniform stability proven under polygon regularity.
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Quantum Simulation of Non-Hermitian Special Functions and Dynamics via Contour-based Matrix Decomposition
CBMD decomposes non-Hermitian operators via contour residues to enable optimal-query quantum simulation of first-order dynamics and special functions such as Bessel and Airy evolutions without requiring diagonalizability.
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AMORE: Adaptive Multi-Output Operator Network for Stiff Chemical Kinetics
AMORE develops an adaptive multi-output DeepONet with custom losses, partition-of-unity trunk, and invertible/softmax mass-fraction maps to surrogate stiff kinetics on syngas (12 states) and GRI-Mech (24 states).
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Robust Containment Queries over Collections of Trimmed NURBS Surfaces via Generalized Winding Numbers
A robust containment query for collections of trimmed NURBS surfaces that computes generalized winding numbers directly via adaptive quadrature on solid angle boundary integrals without surface discretization.
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Laser Powder Bed Fusion Melt Pool Dynamics for Different Geometric Variations and Powder Layer Heights: High-Fidelity Multiphysics Modeling vs 2025 NIST Experiments
High-fidelity multiphysics simulations of laser powder bed fusion melt pools match 2025 NIST experimental data across depth, width, bead height, overlap, and area metrics for varied powder heights and geometries.
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Employing Continuous Integration inspired workflows for benchmarking of scientific software -- a use case on numerical cut cell quadrature
Continuous integration workflows automate benchmarking of numerical cut-cell quadrature across scientific software packages.