A nested Fourier-MIONet surrogate predicts radiative heat transfer in multi-resolution 3D fire simulations with 2-4% error at reduced computational cost compared to direct RTE solves.
Physics- informed machine learning.Nature Reviews Physics, 3(6):422–440
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N-GINNs learn thermodynamically consistent GENERIC dynamics with non-quadratic dissipation potentials from data.
Sparse RFNNs with sSVD via Lanczos-Golub-Kahan bidiagonalization maintain accuracy while improving efficiency and robustness for 1D steady convection-diffusion equations with strong advection.
GeoCert uses hyperbolic geometry to unify forecasting with physical reasoning and built-in formal certification, claiming major gains in accuracy and efficiency.
φ-DeepONet learns mappings with discontinuities in inputs and outputs by combining multiple branch networks with a nonlinear interface embedding in the trunk, trained via physics- and interface-informed loss, and shows accurate results on 1D/2D benchmarks.
A differentiable neural operator learns the mapping from granular microstructure configurations to failure envelopes, with physics-informed convexity enforcement and active learning for efficient training.
citing papers explorer
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Nested Fourier-enhanced neural operator for efficient modeling of radiation transfer in fires
A nested Fourier-MIONet surrogate predicts radiative heat transfer in multi-resolution 3D fire simulations with 2-4% error at reduced computational cost compared to direct RTE solves.
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Nonlinear GENERIC Informed Neural Networks (N-GINNs): learning GENERIC dynamics with non-quadratic dissipation potentials
N-GINNs learn thermodynamically consistent GENERIC dynamics with non-quadratic dissipation potentials from data.
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Sparse Random-Feature Neural Networks with Krylov-Based SVD for Singularly Perturbed ODE
Sparse RFNNs with sSVD via Lanczos-Golub-Kahan bidiagonalization maintain accuracy while improving efficiency and robustness for 1D steady convection-diffusion equations with strong advection.
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GeoCert: Certified Geometric AI for Reliable Forecasting
GeoCert uses hyperbolic geometry to unify forecasting with physical reasoning and built-in formal certification, claiming major gains in accuracy and efficiency.
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$\phi-$DeepONet: A Discontinuity Capturing Neural Operator
φ-DeepONet learns mappings with discontinuities in inputs and outputs by combining multiple branch networks with a nonlinear interface embedding in the trunk, trained via physics- and interface-informed loss, and shows accurate results on 1D/2D benchmarks.
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Neural Operator Representation of Granular Micromechanics-based Failure Envelope
A differentiable neural operator learns the mapping from granular microstructure configurations to failure envelopes, with physics-informed convexity enforcement and active learning for efficient training.