3 papers
cs.LG2025
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…
cs.LG2025
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…
cs.LG2024
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…