Dynamic Simulation

Dynamic simulation aims to accurately and efficiently model the evolution of systems over time, encompassing diverse applications from robotics and materials science to power systems and traffic flow. Current research emphasizes improving simulation speed and accuracy through techniques like reduced-order modeling with neural networks, variational integrators for enhanced physical fidelity, and data-driven approaches such as machine learning for force field prediction and bridging the sim-to-real gap. These advancements are crucial for accelerating scientific discovery, enabling more realistic virtual prototyping, and improving the design and control of complex systems in various engineering domains.