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Found 6 papers, 0 papers with code
A Deep Learning Framework for Solving Hyperbolic Partial Differential Equations: Part I
Physics informed neural networks (PINNs) have emerged as a powerful tool to provide robust and accurate approximations of solutions to partial differential equations (PDEs).
CapsFlow: Optical Flow Estimation with Capsule Networks
We present a framework to use recently introduced Capsule Networks for solving the problem of Optical Flow, one of the fundamental computer vision tasks.
Spatio-Temporal Super-Resolution of Dynamical Systems using Physics-Informed Deep-Learning
In this work, we propose a physics-informed deep learning-based framework to enhance the spatial and temporal resolution of coarse-scale (both in space and time) PDE solutions without requiring any HR data.
PhySRNet: Physics informed super-resolution network for application in computational solid mechanics
Traditional approaches based on finite element analyses have been successfully used to predict the macro-scale behavior of heterogeneous materials (composites, multicomponent alloys, and polycrystals) widely used in industrial applications.
Physics-informed neural networks for modeling rate- and temperature-dependent plasticity
This work presents a physics-informed neural network (PINN) based framework to model the strain-rate and temperature dependence of the deformation fields in elastic-viscoplastic solids.
Machine Learning-Accelerated Computational Solid Mechanics: Application to Linear Elasticity
This work presents a novel physics-informed deep learning based super-resolution framework to reconstruct high-resolution deformation fields from low-resolution counterparts, obtained from coarse mesh simulations or experiments.
