Upcoming Events
- Jul 29 2026
Christopher Jarzynski - Estimating free energy differences with virtually escorted trajectories
- Information
- Wednesday, July 29, 2026 - Wednesday, July 29, 2026
- 10:45 AM - 12:00 PM
- Zoom
Christopher Jarzynski – University of Maryland
Wednesday, July 29, 2026
Zoom opens: 10:30AM EDT
Seminar begins: 10:45AM EDT
Estimating free energy differences with virtually escorted trajectories
The convergence of numerical free energy estimation methods can be accelerated using artificial fields that “escort” simulated trajectories along near-equilibrium paths. Unfortunately, designing such fields is not easy. Taking a cue from the mathematics behind diffusion models – a class of generative models in machine learning – we introduce a method based on virtual escorting. This method adopts a post-processing approach. Given a fixed set of nonequilibrium trajectories, a parameter-dependent virtual escorting field is constructed, possibly using a neural network. This field is used in combination with the trajectories to produce an estimate of the desired free energy difference. The parameters are then adjusted to optimize the convergence of the estimate. I will describe the method, and will discuss conditions under which it produces a zero-variance estimator of the free energy difference.
- Aug 19 2026
Cris Moore - Which links matter most? Sparsifying network dynamics with effective resistance
- Information
- Wednesday, August 19, 2026 - Wednesday, August 19, 2026
- 10:45 AM - 12:00 PM
- Zoom
Cris Moore– Santa Fe Institute
Wednesday, August 19, 2026
Zoom opens: 10:30AM EDT
Seminar begins: 10:45AM EDT
Which links matter most? Sparsifying network dynamics with effective resistance
“Sparsification” is the act of reducing a network to a subset of its edges while approximately preserving its properties: either to reduce the computational cost of solving problems about it, or to identify which edges are the most important in some sense. Computer scientists have developed beautiful techniques for sparsifying a graph using physics-related ideas like the effective resistance. However, while these methods preserve the spectral properties of the Laplacian, it is not obvious to what extent they preserve the behavior of nonlinear dynamical systems. Using a mobility network from the United States as an example, I’ll show that they do very well for the SIR epidemic model, including the probability each node becomes infected and its distribution of arrival times, even when the sparse network includes less than 10% of the original edges. Choosing edges using purely topological methods, or by thresholding edge weights, does not perform nearly as well. I will end by discussing the possibility of using sparsification to “denoise” networks from bioinformatics, and present some preliminary results on the Kuramoto model of coupled oscillators.
This is joint work with Alexander Mercier (Harvard School of Public Health), Emmie Fitz-Gibbons (Brown), and Sam Scarpino (Northeastern).