A graph-based neural operator trained on expert-validated race-car CFD data reaches accuracy levels usable for early-stage interactive aerodynamic design exploration.
Nature Reviews Physics , volume=
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Paper-replication makes coding agents complete SciML paper replications only when every recorded claim has provenance, comparison evidence, and report coverage in a validated workspace.
SPLIT-PINN infers drift fields in Liouville transport equations from data using marginal corrections and orthogonality constraints to enable probabilistic predictions of microstructural evolution across polycrystal realizations.
Learns regionally stable RNN models from input-output data by deriving LMI constraints from generalized sector conditions on deadzone activations and a barrier function to certify forward invariance on a compact set.
A model-agnostic two-stage estimator for conditional quantiles that represents the high-fidelity quantile as a low-fidelity quantile evaluated at a covariate-dependent level, with theory on faster convergence rates under shape similarity.
SVAR-FM uses simulator clamping to produce interventional distributions and flow matching to identify time series causal structures, with an error bound that predicts sign reversal of causal effects below a simulator accuracy threshold.
Geometric Pareto Control embeds Pareto solutions in a Lie group submanifold and navigates via Riemannian gradient flow to achieve 100% feasibility and low suboptimality in control tasks without retraining.
A semi-parametric framework decouples discrepancy functions from physics-based components via orthogonal Gaussian process regression for interpretable nonlinear system identification from incomplete physics.
A differentiable chemistry solver is added to PINNs along with parameterized network architecture and stiffness-tailored residual weighting to solve initial/boundary value problems, inverse parameter identification, and parameterized PDEs for hydrogen combustion.
Shapley values for LLM explanations in financial text are shown via theory and experiments to produce attributions consistent with financial reasoning.
Two deep learning autoregressive models predict the evolution of 2D ideal MHD instabilities while preserving key physical invariants such as global conservation trends and Alfvénic fluctuations.
citing papers explorer
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Faster by Design: Interactive Aerodynamics via Neural Surrogates Trained on Expert-Validated CFD
A graph-based neural operator trained on expert-validated race-car CFD data reaches accuracy levels usable for early-stage interactive aerodynamic design exploration.
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Coding-agents can replicate scientific machine learning papers
Paper-replication makes coding agents complete SciML paper replications only when every recorded claim has provenance, comparison evidence, and report coverage in a validated workspace.
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SPLIT-PINN: Separable Probability Learning Technique via Physics-Informed Neural Networks for High-Dimensional Probabilistic Modeling
SPLIT-PINN infers drift fields in Liouville transport equations from data using marginal corrections and orthogonality constraints to enable probabilistic predictions of microstructural evolution across polycrystal realizations.
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Learning the dynamics of nonlinear systems with regional stability guarantees through linear matrix inequality constraints
Learns regionally stable RNN models from input-output data by deriving LMI constraints from generalized sector conditions on deadzone activations and a barrier function to certify forward invariance on a compact set.
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Multi-Fidelity Quantile Regression
A model-agnostic two-stage estimator for conditional quantiles that represents the high-fidelity quantile as a low-fidelity quantile evaluated at a covariate-dependent level, with theory on faster convergence rates under shape similarity.
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Intervention-Based Time Series Causal Discovery via Simulator-Generated Interventional Distributions
SVAR-FM uses simulator clamping to produce interventional distributions and flow matching to identify time series causal structures, with an error bound that predicts sign reversal of causal effects below a simulator accuracy threshold.
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Geometric Pareto Control: Riemannian Gradient Flow of Energy Function via Lie Group Homotopy
Geometric Pareto Control embeds Pareto solutions in a Lie group submanifold and navigates via Riemannian gradient flow to achieve 100% feasibility and low suboptimality in control tasks without retraining.
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Orthogonal Discrepancy Kernels for Learning with Partial Physics
A semi-parametric framework decouples discrepancy functions from physics-based components via orthogonal Gaussian process regression for interpretable nonlinear system identification from incomplete physics.
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Differentiable Chemistry in PINNs for Solving Parameterized and Stiff Reaction Systems
A differentiable chemistry solver is added to PINNs along with parameterized network architecture and stiffness-tailored residual weighting to solve initial/boundary value problems, inverse parameter identification, and parameterized PDEs for hydrogen combustion.
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Shapley in Context: Explaining Financial Language with Domain Expertise
Shapley values for LLM explanations in financial text are shown via theory and experiments to produce attributions consistent with financial reasoning.
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Autoregressive prediction of 2D MHD dynamics inferred from deep learning modeling
Two deep learning autoregressive models predict the evolution of 2D ideal MHD instabilities while preserving key physical invariants such as global conservation trends and Alfvénic fluctuations.
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