Physics Is Easier Than You Think: From Classical to Neural Elastic Simulation
TL;DR
This course provides a unified journey from classical formulations to modern neural techniques, grounding the audience in the fundamentals of elastostatics and dynamics, and showcases how traditional simulation knowledge translates directly into machine learning loss functions and neural architectures.
Abstract
The demand for high-fidelity, physically-based animation has traditionally been met by sophisticated solvers rooted in elastodynamics and finite element analysis (FEM). Recently, the emergence of neural physics has led to a paradigm shift, transforming neural networks into solvers with memory that dramatically increase the scale and speed of digital environments. Despite its reputation, physics-based simulation does not have to be intimidating. This course aims to demystify the field, proving that these complex systems are accessible and intuitive when approached correctly. We provide a unified journey from classical formulations to modern neural techniques, grounding the audience in the fundamentals of elastostatics and dynamics. We demonstrate how physical problems are discretized via linear finite elements and solved through the elegant lens of optimization. Transitioning into neural physics, we showcase how traditional simulation knowledge translates directly into machine learning loss functions and neural architectures. We analyze strategies for modeling latent spaces for a system’s equilibrium states and to create truly controllable, real-time frameworks. Designed for a broad audience—including students, engineers, researchers, and artists—this course balances theory with practice. To ensure these concepts are immediately actionable, we provide comprehensive reference code for all discussed methods. By the end of the session, attendees will possess the tools to quickly and easily implement their own physics solvers, empowering them to build the next generation of physics-enhanced frameworks and interactive worlds.