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

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

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

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

By using variational IB we could reduce a complex system with multiple dynamical components – cell growth, division, and gene expression ...

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.

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

A prerequisite to model reduction is the identification of these relevant variables, a task for which no general method exists. Here, we develop ...

[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 ...

James V Stone on X: "Information theory for data-driven model ...

Information theory for data-driven model reduction in physics and biology https://t.co/fKwFwMAZTD.

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 ...

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 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 ...

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.

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 ...

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 ...

An Information Theory Approach to Physical Domain Discovery

data using model precision as a metric. The subdo- mains are defined by ... Future development on this method should include the ability to decrease the number of ...

itbook-export CUP/HE2-design August 16, 2024 18:58 Page-i This ...

There are six parts covering foundations of information mea- sures; data compression; hypothesis testing and large deviations theory; channel ...

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.

Information theory - Wikipedia

Information theory is the mathematical study of the quantification, storage, and communication of information. The field was established and put on a firm ...

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

The problems of causality, modeling, and control for chaotic, high-dimensional dynamical systems are formulated in the language of information theory.

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 ...