17 citations · 21 across the 10 of their papers we have counts for
5 papers · 1 filter
NeuFENet: Neural Finite Element Solutions with Theoretical Bounds for Parametric PDEs
Biswajit Khara, Aditya Balu, Ameya Joshi +4
We consider a mesh-based approach for training a neural network to produce field predictions of solutions to parametric partial differential equations (PDEs). This approach contras…
Differentiable Spline Approximations
Minsu Cho, Aditya Balu, Ameya Joshi +6
The paradigm of differentiable programming has significantly enhanced the scope of machine learning via the judicious use of gradient-based optimization. However, standard differen…
Scalable adaptive PDE solvers in arbitrary domains
Kumar Saurabh, Masado Ishii, Milinda Fernando +6
Efficiently and accurately simulating partial differential equations (PDEs) in and around arbitrarily defined geometries, especially with high levels of adaptivity, has significant…
Distributed Multigrid Neural Solvers on Megavoxel Domains
Aditya Balu, Sergio Botelho, Biswajit Khara +6
We consider the distributed training of large-scale neural networks that serve as PDE solvers producing full field outputs. We specifically consider neural solvers for the generali…
Neural-networks model for force prediction in multi-principal-element alloys
Rahul Singh, Prashant Singh, Aayush Sharma +6
Atomistic simulations can provide useful insights into the physical properties of multi-principal-element alloys. However, classical potentials mostly fail to capture key quantum (…