326 citations · 348 across the 7 of their papers we have counts for
7 papers
Biology-informed neural networks learn nonlinear representations from omics data to improve genomic prediction and interpretability
Katiana Kontolati, Rini Jasmine Gladstone, Ian Davis +1
We extend biologically-informed neural networks (BINNs) for genomic prediction (GP) and selection (GS) in crops by integrating thousands of single-nucleotide polymorphisms (SNPs) w…
PINN-FEM: A Hybrid Approach for Enforcing Dirichlet Boundary Conditions in Physics-Informed Neural Networks
Nahil Sobh, Rini Jasmine Gladstone, Hadi Meidani
Physics-Informed Neural Networks (PINNs) solve partial differential equations (PDEs) by embedding governing equations and boundary/initial conditions into the loss function. Howeve…
A Multi-Fidelity Graph U-Net Model for Accelerated Physics Simulations
Rini Jasmine Gladstone, Hadi Meidani
Physics-based deep learning frameworks have shown to be effective in accurately modeling the dynamics of complex physical systems with generalization capability across problem inpu…
GNN-based physics solver for time-independent PDEs
Rini Jasmine Gladstone, Helia Rahmani, Vishvas Suryakumar +3
Physics-based deep learning frameworks have shown to be effective in accurately modeling the dynamics of complex physical systems with generalization capability across problem inpu…
FO-PINNs: A First-Order formulation for Physics Informed Neural Networks
Rini J. Gladstone, Mohammad A. Nabian, N. Sukumar +2
Physics-Informed Neural Networks (PINNs) are a class of deep learning neural networks that learn the response of a physical system without any simulation data, and only by incorpor…
Robust Topology Optimization Using Multi-Fidelity Variational Autoencoders
Rini Jasmine Gladstone, Mohammad Amin Nabian, Vahid Keshavarzzadeh +1
Robust topology optimization (RTO), as a class of topology optimization problems, identifies a design with the best average performance while reducing the response sensitivity to i…
Efficient training of physics-informed neural networks via importance sampling
Mohammad Amin Nabian, Rini Jasmine Gladstone, Hadi Meidani
Physics-Informed Neural Networks (PINNs) are a class of deep neural networks that are trained, using automatic differentiation, to compute the response of systems governed by parti…