60 citations · 121 across the 21 of their papers we have counts for
4 papers · 1 filter
Algorithmically-Consistent Deep Learning Frameworks for Structural Topology Optimization
Jaydeep Rade, Aditya Balu, Ethan Herron +4
Topology optimization has emerged as a popular approach to refine a component's design and increase its performance. However, current state-of-the-art topology optimization framewo…
Solving Inverse Problems in Steady-State Navier-Stokes Equations using Deep Neural Networks
Tiffany Fan, Kailai Xu, Jay Pathak +1
Inverse problems in fluid dynamics are ubiquitous in science and engineering, with applications ranging from electronic cooling system design to ocean modeling. We propose a genera…
An unsupervised learning approach to solving heat equations on chip based on Auto Encoder and Image Gradient
Haiyang He, Jay Pathak
Solving heat transfer equations on chip becomes very critical in the upcoming 5G and AI chip-package-systems. However, batches of simulations have to be performed for data driven s…
DiscretizationNet: A Machine-Learning based solver for Navier-Stokes Equations using Finite Volume Discretization
Rishikesh Ranade, Chris Hill, Jay Pathak
Over the last few decades, existing Partial Differential Equation (PDE) solvers have demonstrated a tremendous success in solving complex, non-linear PDEs. Although accurate, these…