Exploring Neural Operators, PINNs, and the intersection of deep learning with physical systems.
Exploring how machine learning accelerates CFD simulations, from physics-informed neural networks to neural operators for fluid flow prediction.
How neural networks can model and predict complex dynamical systems in engineering applications, from Neural ODEs to operator learning.
A novel Neural ODE architecture that enforces physical consistency between displacement and velocity states, enabling interpretable latent dynamics and virtual sensing.
A comprehensive review of the PEML spectrum—ranging from white-box Bayesian filters to dark-gray physics-encoded learners—demonstrated on a Duffing oscillator.
A framework combining Variational Autoencoders with Physics-informed Neural ODEs and modal decomposition to reconstruct full-field structural dynamics from sparse sensor data.
Nonlinear pendulum simulation demonstrating chaos theory.