Aspen Center for Physics · Workshop · 2027

World Modeling for Physics

A five-day workshop on learning predictive models of the physical world — from joint-embedding predictive architectures to world models that reason in latent space.

Feb 28 – Mar 5, 2027 Aspen, Colorado ~70 participants
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Lorenz system · σ 10  ρ 28  β 8/3
a strange attractor in phase space
DatesFeb 28 – Mar 5 · 2027
VenueAspen Center · for Physics
FormatTalks + open afternoons
Participants~70 · invitation & CFP
01

A meeting point for two predictive traditions

World models and joint-embedding predictive architectures (JEPA) start from a simple premise: intelligence rests on internal models that predict how the world evolves — learned in abstract representation spaces rather than raw pixels or measurements.

Physics has built predictive models of the world from first principles for centuries. This workshop asks what these two traditions can learn from each other. Can self-supervised, joint-embedding methods recover physical structure — symmetries, conservation laws, dynamics — directly from data? Can physical priors make world models more sample-efficient, interpretable, and reliable?

Over five days at the Aspen Center for Physics, roughly seventy researchers from machine learning and the physical sciences work through these questions in focused morning sessions and long, unstructured afternoons built for collaboration.

“The world is predictable in the right coordinates. The open question is whether a model can learn those coordinates on its own.”
— Workshop premise
02

Themes

T1

Joint-embedding prediction

JEPA and self-supervised representation learning as a route to abstract, predictive features.

T2

World models & latent dynamics

Learning state spaces, rollouts, and model-based planning over learned representations.

T3

Symmetry & physical priors

Conservation laws, equivariance, and geometry as inductive biases for learning.

T4

Foundation models for science

Large pretrained models for physics, astronomy, and the natural sciences.

T5

Emergence & interpretability

What physical structure emerges in learned dynamics, and how to read it.

T6

Predictive vs. generative

Energy-based and predictive objectives against generative modeling in latent space.

03

Call for Papers

We invite contributions at the intersection of representation learning and the physical sciences. Both empirical and theoretical work is welcome, including position papers that frame open problems.

  • Extended abstracts up to 4 pages, plus references and appendix.
  • Single-blind review; preprints are permitted and encouraged.
  • Accepted work is presented as a talk or poster; no proceedings, preprint-friendly.
  • Submissions via OpenReview (link to be announced).
Submit on OpenReview →
OCT 2026
Submissions open
Portal goes live on OpenReview.
DEC 15, 2026
Submission deadline
23:59 AoE, no extensions.
JAN 15, 2027
Notifications
Decisions and reviews returned.
FEB 01, 2027
Camera-ready & registration
Final materials and travel confirmation.
FEB 28 – MAR 5, 2027
Workshop
Aspen Center for Physics.
04

Program

Participants arrive Sunday, February 28, with an evening welcome reception; sessions run Monday through Friday. Afternoons are deliberately kept open — the Aspen tradition of unstructured time for collaboration, working groups, and the occasional ski. Speakers to be announced after review.
05

Venue

The workshop is hosted at the Aspen Center for Physics, an independent institution that has convened physicists for focused, collaborative research since 1962. Its format — short mornings, open afternoons, no distractions — is the reason the meeting is structured the way it is.

Aspen sits at 2,400 m in the Elk Mountains of Colorado. Late February is deep winter: bring layers. Lodging and travel guidance will be shared with registered participants.

39.1898° N, 106.8197° W · 2438 m
Aspen Center for Physics
700 W Gillespie St, Aspen, Colorado 81611
Open in Google Maps ↗
Founded
1962
Nearest air
ASE / DEN
Elevation
8,000 ft
06

Organizers

RBRandall Balestriero
Randall Balestriero
Brown University
SHShirley Ho
Shirley Ho
New York University
JKJulia Kempe
Julia Kempe
New York University
YLYann LeCun
Yann LeCun
New York University