Krishna Kumar
Modeling complex physical systems governed by partial differential equations (PDEs) is a fundamental challenge across many civil engineering domains. Traditional numerical methods like finite element analysis can struggle with high-dimensional parametric PDEs or cases with limited training data. Physics-informed machine learning (PIML) provides a powerful alternative by combining neural networks with the governing physics described by PDEs. This webinar explores the core methodology of PIML and its applications through hands-on training. PIML embeds the known physics directly into the neural network architecture, either as hard constraints or via additional loss terms derived from the PDE residuals. The neural network then approximates the unknown solution while inherently satisfying the specified physical laws. We illustrate PIML techniques through examples of modeling nonlinear PDEs like Burgers’ equation describing fluid flows and heat flow. We will also discuss inverse problems estimating the PDE parameters. The flexibility and physics-grounding of PIML make it a broadly applicable tool for diverse civil engineering disciplines.