Constructing Custom Thermodynamics Using Deep Learning

• URL: http://arxiv.org/abs/2308.04119v3
• Date: Fri, 22 Dec 2023 06:12:20 GMT
• Title: Constructing Custom Thermodynamics Using Deep Learning
• Authors: Xiaoli Chen, Beatrice W. Soh, Zi-En Ooi, Eleonore Vissol-Gaudin, Haijun Yu, Kostya S. Novoselov, Kedar Hippalgaonkar, Qianxiao Li
• Abstract summary: One of the most exciting applications of artificial intelligence (AI) is automated scientific discovery based on previously amassed data. Here we develop a platform based on a generalized Onsager principle to learn macroscopic descriptions of arbitrary dissipative systems. We demonstrate its effectiveness by studying theoretically and experimentally validating the stretching of long polymer chains in an externally applied field.
• Score: 10.008895786910195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the most exciting applications of artificial intelligence (AI) is automated scientific discovery based on previously amassed data, coupled with restrictions provided by known physical principles, including symmetries and conservation laws. Such automated hypothesis creation and verification can assist scientists in studying complex phenomena, where traditional physical intuition may fail. Here we develop a platform based on a generalized Onsager principle to learn macroscopic dynamical descriptions of arbitrary stochastic dissipative systems directly from observations of their microscopic trajectories. Our method simultaneously constructs reduced thermodynamic coordinates and interprets the dynamics on these coordinates. We demonstrate its effectiveness by studying theoretically and validating experimentally the stretching of long polymer chains in an externally applied field. Specifically, we learn three interpretable thermodynamic coordinates and build a dynamical landscape of polymer stretching, including the identification of stable and transition states and the control of the stretching rate. Our general methodology can be used to address a wide range of scientific and technological applications.

Related papers

• Geometric Trajectory Diffusion Models [58.853975433383326]
  Generative models have shown great promise in generating 3D geometric systems. Existing approaches only operate on static structures, neglecting the fact that physical systems are always dynamic in nature. We propose geometric trajectory diffusion models (GeoTDM), the first diffusion model for modeling the temporal distribution of 3D geometric trajectories.
  arXiv  Detail & Related papers  (2024-10-16T20:36:41Z)
• Automated Discovery of Continuous Dynamics from Videos [4.690264156292023]
  We propose an approach to discover a set of state variables that preserve the smoothness of the system dynamics. We construct a vector field representing the system's dynamics equation, automatically from video streams without prior physical knowledge.
  arXiv  Detail & Related papers  (2024-10-14T03:37:02Z)
• Decomposing heterogeneous dynamical systems with graph neural networks [0.16492989697868887]
  We show that graph neural networks can be designed to jointly learn the interaction rules and the structure of the heterogeneous system. The learned latent structure and dynamics can be used to virtually decompose the complex system.
  arXiv  Detail & Related papers  (2024-07-27T04:03:12Z)
• Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction [41.22779249609767]
  We show how to use MDP homomorphisms formalism to exploit known geometric structures via abstraction. We also present the first analysis that formally captures the benefit of abstraction via homomorphisms on sample efficiency. We propose the Geometric Active Exploration (GAE) algorithm, which we analyse theoretically and experimentally in environments motivated by problems in scientific discovery.
  arXiv  Detail & Related papers  (2024-07-18T10:15:51Z)
• LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
  We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations. We introduce Scientific Generative Agent (SGA), a bilevel optimization framework. We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
  arXiv  Detail & Related papers  (2024-05-16T03:04:10Z)
• Morphological Symmetries in Robotics [45.32599550966704]
  morphological symmetries are intrinsic properties of the robot's morphology. These symmetries extend to the robot's state space and sensor measurements. For data-driven methods, we demonstrate that morphological symmetries can enhance the sample efficiency and generalization of machine learning models. In the context of analytical methods, we employ abstract harmonic analysis to decompose the robot's dynamics into a superposition of lower-dimensional, independent dynamics.
  arXiv  Detail & Related papers  (2024-02-23T17:21:21Z)
• Discovering Interpretable Physical Models using Symbolic Regression and Discrete Exterior Calculus [55.2480439325792]
  We propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models. DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems. We prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data.
  arXiv  Detail & Related papers  (2023-10-10T13:23:05Z)
• TANGO: Time-Reversal Latent GraphODE for Multi-Agent Dynamical Systems [43.39754726042369]
  We propose a simple-yet-effective self-supervised regularization term as a soft constraint that aligns the forward and backward trajectories predicted by a continuous graph neural network-based ordinary differential equation (GraphODE) It effectively imposes time-reversal symmetry to enable more accurate model predictions across a wider range of dynamical systems under classical mechanics. Experimental results on a variety of physical systems demonstrate the effectiveness of our proposed method.
  arXiv  Detail & Related papers  (2023-10-10T08:52:16Z)
• Evolution TANN and the discovery of the internal variables and evolution equations in solid mechanics [0.0]
  We propose a new approach which allows, for the first time, to decouple the material representation from the incremental formulation. Inspired by the Thermodynamics-based Artificial Neural Networks (TANN) and the theory of the internal variables, the evolution TANN (eTANN) are continuous-time. Key feature of the proposed approach is the discovery of the evolution equations of the internal variables in the form of ordinary differential equations.
  arXiv  Detail & Related papers  (2022-09-27T09:25:55Z)
• AI Research Associate for Early-Stage Scientific Discovery [1.6861004263551447]
  Artificial intelligence (AI) has been increasingly applied in scientific activities for decades. We present an AI research associate for early-stage scientific discovery based on a novel minimally-biased physics-based modeling.
  arXiv  Detail & Related papers  (2022-02-02T17:05:52Z)
• Integration of Data and Theory for Accelerated Derivable Symbolic Discovery [3.7521856498259627]
  We develop a methodology combining automated theorem proving with symbolic regression, enabling principled derivations of laws of nature. We demonstrate this for Kepler's third law, Einstein's relativistic time dilation, and Langmuir's theory of adsorbing. The combination of logical reasoning with machine learning provides generalizable insights into key aspects of the natural phenomena.
  arXiv  Detail & Related papers  (2021-09-03T17:19:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.