Title: Hidden Physics Models: Machine Learning of Non-Linear Partial Differential Equations

Speaker: Maziar Raissi (Brown University)

Abstract: A grand challenge with great opportunities is to develop a coherent framework that enables blending conservation laws, physical principles, and/or phenomenological behaviors expressed by differential equations with the vast data sets available in many fields of engineering, science, and technology. At the intersection of probabilistic machine learning, deep learning, and scientific computations, this work is pursuing the overall vision to establish promising new directions for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data. To materialize this vision, this work is exploring two complementary directions: (1) designing data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and non-linear differential equations, to extract patterns from high-dimensional data generated from experiments, and (2) designing novel numerical algorithms that can seamlessly blend equations and noisy multi-fidelity data, infer latent quantities of interest (e.g., the solution to a differential equation), and naturally quantify uncertainty in computations. The latter is aligned in spirit with the emerging field of probabilistic numerics.

@UCSC: E2-215 at 12pm PT

Online:  https://ucsc.zoom.us/j/468337241

When/where: E2-215 at 12pm

Presenter: Oskar Elek
Title: Monte Carlo Physarum Machine: Unconventional AI for astronomy and beyond

Abstract: Slime mold (Physarum Polycephallum) is a freak of the natural world that – out of decaying forest debris – builds near-optimal transport networks. We leverage a custom Monte-Carlo simulation of this organism to approximate such transport networks: both in terms of structure and their density (likelihood) in 3D space. I will discuss our analysis of the Cosmic Web using this hybrid model, as well as future prospects in training and adapting it to several other domains.

With luck this Zoom link will work.

When/where: E2-215 at 12pm

Presenter: Reuben Harry Cohn-Gordon (Stanford University)
Title: Bayesian Models of Pragmatics for Natural Language

Abstract: Emerging from work in Bayesian cognitive science and game theory, probabilistic models of pragmatic reasoning have been successful at modelling human inferences about linguistic meaning, on the basis of their interlocutor’s intentions and knowledge. However, they have largely been applied to idealized domains. Meanwhile, natural language processing systems have made significant progress at open-domain tasks requiring language understanding, but often struggle to behave in human-like ways. I describe work combining these approaches, with the aim of obtaining interpretable models of pragmatic reasoning for natural language.