GitHub

Finite basis physics-informed neural networks (FBPINNs)

This repository allows you to solve forward and inverse problems related to partial differential equations (PDEs) using finite basis physics-informed neural networks (FBPINNs). To improve the ...
archive

A Book Of Problems In Ordinary Differential Equations

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Chemical Engineering

New Technologies Optimize Mixing Processes

Advanced designs and controls achieve complete mixing while reducing batch times In the chemical process industries (CPI), ...
IEEE

On the Converse Safety Problem for Differential Inclusions: Solutions, Regularity, and Time-Varying Barrier Functions

Abstract: This article presents converse theorems for safety in terms of barrier functions for unconstrained continuous-time systems modeled as differential inclusions. Via a counterexample, we show ...
IEEE

Solving Strongly Nonlinear Inverse Scattering Problems for Mixture of Dielectric and PEC With Novel Contraction T-Matrix Equation

Abstract: The classical inverse scattering methods have been developed to reformulate the Lippmann-Schwinger integral equation (LS-IE) for mitigating the nonlinearity, such as the new integral ...