Mathematical Physics Webinar
Upcoming Events
- Jul 22 2026
Uri Alon - Why is the upper tail of human lifespan so rigid? Insights from Langevin threshold-crossing models of aging
- Information
- Wednesday, July 22, 2026 - Wednesday, July 22, 2026
- 10:45 AM - 12:00 PM
- Zoom
Uri Alon - Weizmann Institute
Wednesday, July 22, 2026
Zoom opens: 10:30AM EDT
Seminar begins: 10:45AM EDT
Why is the upper tail of human lifespan so rigid? Insights from Langevin threshold-crossing models of aging
Human life expectancy has doubled over the last two centuries, yet the upper tail of human lifespan has barely moved. Why is median lifespan so plastic while extreme longevity remains so rigid? I will present a stochastic threshold-crossing view of aging, in which physiological damage follows time-dependent Langevin dynamics and death occurs as a first-passage event across a critical threshold. In this framework, the exponential increase in mortality with age corresponds to escape from a linearly declining barrier. The model separates two classes of parameters: **robustness parameters**, such as noise amplitude and threshold height, which affect the probability of crossing the barrier, and **senogenic parameters**, which govern the deterministic damage production and removal. Changes in robustness can improve median survival while preserving extreme-lifespan distributions, whereas changes in senogenic parameters shift extreme longevity. I will discuss how this distinction helps interpret historical mortality improvements, lifestyle and socioeconomic effects, which all seem to only affect robustness parameters, as well as familial longevity, and progeroid diseases.
- 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.