Bridging Control, Inference, Transport, and Thermodynamics: From Theory to Applications in Learning.

Emmy Blumenthal, Nikolas Claussen, Benjamin Eysenbach, Catherine Ji, Gautam Reddy, Colin Scheibner, Benjamin Sorkin

Abstract readPreprint

In one paragraph

Article in ArXiv, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Emmy BlumenthalJoseph Henry Laboratories of Physics, Princeton University, Princeton, New Jersey 08544, USA.

Nikolas ClaussenJoseph Henry Laboratories of Physics, Princeton University, Princeton, New Jersey 08544, USA.

Benjamin EysenbachDepartment of Computer Science, Princeton University, Princeton, New Jersey 08544, USA.

Catherine JiJoseph Henry Laboratories of Physics, Princeton University, Princeton, New Jersey 08544, USA.

Gautam ReddyJoseph Henry Laboratories of Physics, Princeton University, Princeton, New Jersey 08544, USA.

Colin ScheibnerJoseph Henry Laboratories of Physics, Princeton University, Princeton, New Jersey 08544, USA.

Benjamin SorkinJoseph Henry Laboratories of Physics, Princeton University, Princeton, New Jersey 08544, USA.

Funding

Thermally robust chemotaxis and thermotaxis in Escherichia coliR01GM082938 · NIGMS · PRINCETON UNIVERSITY · PI WINGREEN, NED S · 2008 to 2025

$5.5M

Learning biophysical principles ofcellular organizationR35GM156427 · NIGMS · PRINCETON UNIVERSITY · PI NED S WINGREEN · 2025 to 2026

$861k

NIGMS NIH HHS R01 GM082938NIGMS NIH HHS R35 GM156427

6 · The paper itself

Abstract

The last decade has seen the development of powerful methods for learning complex structure from high-dimensional data. These advances have brought to the foreground fundamental connections between subdisciplines of physics, applied mathematics, and machine learning. In this review, we bring together some of these ideas, often expressed in different languages, to highlight a conceptual thread that links five distinct fields: control theory, optimal transport, probabilistic inference, non-equilibrium thermodynamics, and machine learning. A common theme is the optimization of free-energy-like functionals under dynamical or statistical constraints. We offer a guided tour through this thread and present selected applications in reinforcement learning, variational inference, and generative modeling. The review does not assume prior familiarity with these topics, and begins with principles originating from physics.

Identifiers

PMID42780301

PMCPMC13596642

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.