PEIL learns unobservable parameters by embedding them in a physics-based reconstruction loop, outperforming supervised baselines with ground-truth access while enabling zero-shot generalization and major data reduction in wireless and MRI tasks.
& Thuerey, N
5 Pith papers cite this work, alongside 44 external citations. Polarity classification is still indexing.
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Hybrid CFD-MOMARL framework with PCGrad enables micro-swarm navigation in pulsatile flow, achieving progress 6.5-7.0, energy 0.63-0.65, smoothness 0.97-0.99 with emergent behaviors.
An analytical adjoint with smoothed Fischer–Burmeister contact and a Woodbury-preconditioned Krylov solver yields stable gradients for frictional deformable contact.
Physical neural substrates realize inference and adaptation via native physics and occupy complementary regimes; no single platform dominates the proposed static/dynamic benchmarks.
PODR precomputes a proper orthogonal decomposition basis from classical solutions to project quantum states onto a minimal set of coefficients for reconstruction, reducing measurements in online quantum simulations.
citing papers explorer
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Discovery of unobservable parameters via physical embedding
PEIL learns unobservable parameters by embedding them in a physics-based reconstruction loop, outperforming supervised baselines with ground-truth access while enabling zero-shot generalization and major data reduction in wireless and MRI tasks.
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Micro-Swarm Locomotion Optimization in Dynamic Flow using Multi-Objective Multi-Agent Reinforcement Learning
Hybrid CFD-MOMARL framework with PCGrad enables micro-swarm navigation in pulsatile flow, achieving progress 6.5-7.0, energy 0.63-0.65, smoothness 0.97-0.99 with emergent behaviors.
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Fast and Reliable Gradients for Deformables Across Frictional Contact Regimes
An analytical adjoint with smoothed Fischer–Burmeister contact and a Woodbury-preconditioned Krylov solver yields stable gradients for frictional deformable contact.
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Beyond Silicon: Materials, Mechanisms, and Methods for Physical Neural Computing
Physical neural substrates realize inference and adaptation via native physics and occupy complementary regimes; no single platform dominates the proposed static/dynamic benchmarks.
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Problem-Specific Basis Quantum State Readout via Proper Orthogonal Decomposition
PODR precomputes a proper orthogonal decomposition basis from classical solutions to project quantum states onto a minimal set of coefficients for reconstruction, reducing measurements in online quantum simulations.