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#surrogate-models

4 curated events
papersTODAY 04:00 UTC

Active Learning with Bayesian Multi-Fidelity Laplace Neural Operators for Parametric PDEs

This arXiv paper proposes a surrogate modeling approach that combines Laplace neural operators with multi-fidelity Bayesian active learning for oscillatory parametric partial differential equations. High-fidelity simulation data is costly to generate, so the method uses Bayesian uncertainty estimates to decide which fidelity levels and parameter points to sample next. The authors frame the work around engineering uses such as design optimization and digital twins, where fast, repeated predictions are needed.

research/active-learning/pde/surrogate-models
papersSEP 10 04:00 UTC

Graph Neural Operator Surrogate Predicts Stress Tensor Fields in Concrete Penetration

Researchers have developed a graph neural operator that estimates full mid-plane stress tensor fields in concrete during projectile penetration, connecting the material's mesoscale structure to complete field-level predictions. The surrogate was trained on data from a detailed aggregate-resolved LS-DYNA simulation and is designed to generalize across different impact velocities, offering a faster stand-in for expensive finite-element computations.

graph-neural-networks/surrogate-models/simulation/materials-science
papersSEP 10 04:00 UTC

Convolutional autoencoder and neural ODE framework for transient counterflow flame modeling

Researchers propose a reduced-order modeling framework that combines a convolutional autoencoder with a neural ordinary differential equation to serve as a surrogate for simulating transient two-dimensional counterflow flames. The approach extends autoencoder–neural ODE techniques, previously applied to homogeneous reactive systems, to spatially resolved combustion problems. Such surrogates can cut the computational cost of modeling reactive flows.

scientific-ml/neural-ode/combustion/surrogate-models