Stability and Inference for Position-Dependent Langevin Diffusions

Explore the cutting-edge research on stability and inference for position-dependent Langevin diffusions in this 37-minute lecture by Vincent Danos at the Alan Turing Institute. Delve into the emerging paradigm of combining statistical inference, high-throughput computation, and physical laws to model complex systems. Discover how this approach is revolutionizing various scientific fields, including collective dynamics, molecular modeling, cell biology, and fluid dynamics. Learn about the challenges in identifying crucial model features and the powerful statistical methods being developed to address them. Gain insights into the mathematical foundations underpinning this new modeling approach and its potential to transform scientific prediction and computation across disciplines.

Vincent Danos (DDMCS@Turing): Stability and inference for position-dependent Langevin diffusions