IEEE

Bootstrap-Driven Adaptive Resampling for Enhanced Solutions of Second-Order Differential Equations in RLC Circuits Using PINNs

Abstract: Physics-informed neural networks (PINNs) offer a flexible framework for solving differential equations using physical constraints and data. This study focuses on second-order ...
IEEE

Federated Scientific Machine Learning for Approximating Functions and Solving Differential Equations With Data Heterogeneity

Abstract: By leveraging neural networks, the emerging field of scientific machine learning (SciML) offers novel approaches to address complex problems governed by partial differential equations (PDEs) ...
GitHub

DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations

TensorFlow implementation for DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations. Physics-informed neural networks are a type of promising tools to ...
GitHub

Game_Theoretic_Satellite_Formation_Flying

Solving the 2-player Zero-Sum differential game using GARE (Kleinman) Solving the 2-player Zero-Sum differential game using Online Optimal Adaptive Control using Neural Network Solving the 2-player ...