Year: 2026
Type: article
Abstract: Standard approaches to controlling dynamical systems involve biologically implausible steps such as backpropagation of errors or intermediate model-based system representations. Recent advances in machine learning have shown that "imperfect" feedback of errors during training can yield test performance that is similar to using full backpropagated errors, provided that the two error signals are at least somewhat aligned. Inspired by such methods, we introduce an iterative, spatiotemporally local protocol to learn driving forces and control non-equilibrium dynamical systems using imperfect feedback signals. We present numerical experiments and theoretical justification for several examples. For systems in conservative force fields that are driven by external time-dependent protocols, our update rules resemble a dynamical version of contrastive divergence. We appeal to linear response theory to establish that our imperfect update rules are locally convergent for these conservative systems. Finally, we show that similar local update rules can also solve dynamical control problems for non-conservative systems, and we illustrate this by learning spatiotemporal activity fields that pull topological defects along desired trajectories in an active nematic fluid. These imperfect feedback methods are information efficient, and they can help extend recent methods of decentralized training for physical materials into dynamical settings.
Source: Newton
Institution: University of Chicago
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $5,460
Awards: G1945133785, G2510409749, Materials Research Science and Engineering Center, G4657484576, G8048112891, Collaborative Research: Understanding and controlling force generation by a centrin-based contractile system, DESIGNING ADAPTIVE INFORMATION PROCESSING MATERIALS USING NON-EQUILIBRIUM FORCING
OpenAlex ID: W7171442946