3 papers
cs.LG2025
DAE-HardNet: A Physics Constrained Neural Network Enforcing Differential-Algebraic Hard Constraints
Rahul Golder, Bimol Nath Roy, M. M. Faruque Hasan
Traditional physics-informed neural networks (PINNs) do not always satisfy physics based constraints, especially when the constraints include differential operators. Rather, they m…
cs.LG2025
Physics-Informed Neural Networks with Hard Nonlinear Equality and Inequality Constraints
Ashfaq Iftakher, Rahul Golder, Bimol Nath Roy +1
Traditional physics-informed neural networks (PINNs) do not guarantee strict constraint satisfaction. This is problematic in engineering systems where minor violations of governing…
cs.LG2025
Discovering Interpretable Ordinary Differential Equations from Noisy Data
Rahul Golder, M. M. Faruque Hasan
The data-driven discovery of interpretable models approximating the underlying dynamics of a physical system has gained attraction in the past decade. Current approaches employ pre…