A differentiable-physics solver reconstructed wall shear stress accurately from limited passive-scalar data in 2D and 3D flow benchmarks, outperforming physics-informed neural networks in most scenarios.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
representative citing papers
Dynamics-encoded deep learning approaches are developed for system identification and parameter estimation in dynamical systems using numerical discretization schemes.
This perspective paper categorizes hybrid architectures for combining mechanistic and data-driven models using residual learning, Neural ODEs, and solver-in-the-loop to model neurological disorder progression.
citing papers explorer
-
Wall Shear Stress Reconstruction from Concentration: Differentiable Physics and Physics-Informed Neural Networks
A differentiable-physics solver reconstructed wall shear stress accurately from limited passive-scalar data in 2D and 3D flow benchmarks, outperforming physics-informed neural networks in most scenarios.
-
Dynamics-Encoded Deep Learning for Robust System Identification and Parameter Estimation
Dynamics-encoded deep learning approaches are developed for system identification and parameter estimation in dynamical systems using numerical discretization schemes.
-
Integrating Mechanistic and Data-Driven Models for Neurological Disorders through Differentiable Programming
This perspective paper categorizes hybrid architectures for combining mechanistic and data-driven models using residual learning, Neural ODEs, and solver-in-the-loop to model neurological disorder progression.