BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Talks.cam//talks.cam.ac.uk// X-WR-CALNAME:Talks.cam BEGIN:VEVENT SUMMARY:Using Data-driven Methods to Understand and Control Active Materia ls - Michael Hagan (Brandeis University) DTSTART:20250910T133000Z DTEND:20250910T141000Z UID:TALK233293@talks.cam.ac.uk DESCRIPTION:Active matter is composed of particles that generate forces or motion\, which leads to spectacular emergent dynamics. In principle\, act ive matter could form the basis for a new class of materials with lifelike properties of biological organisms. Yet\, active materials exhibit divers e dynamical states\, most of which have chaotic dynamics that do not produ ce work or other useful functions. Thus\, a robust control strategy is nee ded to drive active materials into an emergent state that corresponds to a desirable function. However\, designing control protocols requires accura te dynamical models\, which are not available for most active matter syste ms. Developing quantitative models using traditional statistical physics a pproaches is challenging because active materials lack the scale separatio n characteristic of equilibrium systems. \;In this presentation\, I wi ll discuss efforts to combine machine learning\, other data-driven techniq ues\, and  \;physics-based models with control theory to address this challenge in the context of a widely-used active material\, microtubule-ba sed active nematics. Recent advances in optogenetic motors have enabled co nstructing the light-activated active nematics\, in which the activity can be spatiotemporally controlled by shining light on the sample. The challe nge is to determine the spatiotemporal light sequence required to drive th e system into a desired behavior.I will describe two complementary approac hes to computationally determine an optimal light sequence. In the first\, we have adapted a method to discover optimal physics-based continuum mode ls directly from spatiotemporal data\, using sparse regression. We have id entified several approaches to mitigate measurement errors in the data. We find that the method can reveal the relative contributions of different p hysical mechanisms\, and quantitatively estimates key experimental paramet ers. Then\, we have developed a framework to combine the optimal physics-b ased model with optimal control theory to solve for the spatiotemporal act ivity profile that drives the system into a desired state. We demonstrate that active materials can be driven into arbitrary behaviors\, including t hose which do not correspond to dynamical attractors and thus cannot be ac cessed without control.Since no model is perfectly accurate for a specific system\, in the second approach we develop a deep reinforcement learning (DRL) based controller to enable model-free control of active materials. T he controller discovers and implements spatiotemporal sequences of activit y to drive a 2D active nematic system toward a prescribed dynamical steady -state. This framework does not require a detailed physics model\, making it ideal for complex active materials that lack quantitative theoretical d escriptions. Furthermore\, the approach is extremely robust to noise and e xperimental measurement error. We compare the performance of the physics-b ased and DRL-based controllers for active nematics.This work was supported by DE-SC0022291. Preliminary work was supported by NSF DMR-1855914 and DM R-2011846. Computing resources were provided by XSEDE TG-MCB090163 and the Brandeis HPCC (DMR-MRSEC 2011846 and OAC-1920147). LOCATION:External END:VEVENT END:VCALENDAR