Rigorous engineering perspectives on computational fluid dynamics, non-linear continuum mechanics, physics-informed AI surrogates, and modern CAE design optimization.
How embedding mass, momentum, and energy conservation residuals directly into deep learning loss functions allows engineers to achieve millisecond parametric flow predictions with rigorous physical fidelity.
Why static yield criteria consistently fail to predict cyclic fatigue failure in structural weldments, and how critical plane algorithms (Findley, Dang Van) deliver accurate life cycle estimates.
Modeling transient thermal runaway propagation, anisotropic heat conduction across pouch layers, and dual-surface cold plate cooling architectures under aggressive fast-charge protocols.
Our computational mechanics group collaborates with corporate R&D divisions and academic labs to co-develop physics models and numerical solvers.