Learning Low-Dimensional Quadratic-Embeddings of High-Fidelity Nonlinear Dynamics using Deep Learning

Understand

Learning dynamical models from data plays a vital role in engineering design, optimization, and predictions.

• Building models describing dynamics of complex processes (e.g., weather dynamics, or reactive flows) using empirical knowledge or first principles are onerous or infeasible.
• Moreover, these models are high-dimensional but spatially correlated.
• It is, however, observed that the dynamics of high-fidelity models often evolve in low-dimensional manifolds.