Information theory for data|driven model reduction in physics ...

Information theory for data-driven model reduction in physics and ...

Model reduction is the construction of simple yet predictive descriptions of the dynamics of many-body systems in terms of a few relevant ...

Information theory for data-driven model reduction in physics ... - arXiv

Here, we develop a systematic approach based on the information bottleneck to identify the relevant variables, defined as those most predictive of the future.

[PDF] Information theory for data-driven model reduction in physics ...

Information theory for data-driven model reduction in physics and biology · Matthew S. Schmitt, Maciej Koch-Janusz, +3 authors. Vincenzo Vitelli · Published in ...

Information theory for data-driven model reduction in physics and ...

Information theory for data-driven model reduction in physics and biology. arXiv.physics.bio-ph. Pub Date : 2023-12-11. DOI : arxiv-2312.06608.

Information theory for data-driven model reduction in physics and ...

Information theory for data-driven model reduction in physics and biology. View PDF. By Matthew S. Schmitt, Maciej Koch-Janusz, Michel ...

Data-driven, variational model reduction of high-dimensional ...

In this work we present new scalable, information theory-based variational methods for the efficient model reduction of high-dimensional deterministic and ...

Data-driven and physics-based modelling of process behaviour and ...

The consideration of physical laws in data-driven modelling has recently been shown to enable enhanced prediction performance and generalization while requiring ...

Information theory for data-driven model reduction in physics and ...

Extracting Slow Collective Variables from High-Dimensional Dynamical Systems Using Information Theory · Core Concepts. Information theory provides a systematic ...

Data-driven physics-based digital twins via a library of component ...

Model order reduction [8–11] provides a mathematical foundation for accelerating complex computational models so that they may be operationalized in the digital ...

Debates: Does Information Theory Provide a New Paradigm for ...

Scientific modeling parsimony can be quantified using the data compression paradigm by measuring the sum of lossy model size and the information ...

A deeper look into natural sciences with physics-based and data ...

Several model-based data analysis tools have been inspired by information theory and physics principles in order to extract relevant features from the data.

Interpretable and Explainable Data-Driven Methods for Physical ...

Description: A data-driven model can be built to accurately accelerate computationally expensive physical simulations, which is essential in ...

Information-theoretic formulation of dynamical systems: Causality ...

Modeling can be posed as a problem of conservation of information: Reduced-order models contain a smaller number of degrees of freedom than the ...

A Tale of Two Approaches: Physics-Based vs. Data-Driven Models

Most theoretical methods used in the industry are the result of deriving differential equations that are based on conservation laws, physical ...

Information theory for data-driven model reduction in physics and ...

Model reduction is the construction of simple yet predictive descriptions of the dynamics of many-body systems in terms of a few relevant variables.

Learning dynamical systems from data: An introduction to physics ...

DL, on the other hand, is purely data-driven: Statistical models representing massive data are used to make predictions about the real world. DL ...

Learning physics-based models from data: perspectives from ...

In both cases, the result is a predictive model that reflects data-driven learning yet deeply embeds the underlying physics, and thus can be ...

Physics-based and data-driven hybrid modeling in manufacturing

Failing to achieve correct parameterization can reduce the model's robustness, and errors stemming from imperfect parameterization can adversely impact other ...

An Information-Theory-Based Approach for Optimal Model ...

In theoretical modeling of a physical system, a crucial step consists of the identification of those degrees of freedom that enable a ...

Data-driven discovery of reduced plasma physics models from fully ...

Our findings show that this data-driven methodology offers a promising route to accelerate the development of reduced theoretical models of ...