reduced-order modeling

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papersTODAY 04:00 UTC

Linearized PINN Uses Pretrained Nonlinear Layers for Solving Differential Equations

Researchers introduce lPINN, a reduced-order neural basis approach for both forward and inverse differential equation problems. In an offline phase, the method derives operator-compatible continuous neural basis functions from an ensemble, then solves equations in a lower-dimensional space. The work aims to combine physics-informed modeling with the efficiency of pretrained linearized representations.

research/physics-informed/neural-networks/differential-equations

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