[KongchangAI]
ConceptConstrained Learning for Scientific Modelling / 科学建模约束学习

CL4SM

The Constraint Learning for Scientific Modeling (CL4SM) workshop is a NeurIPS pre-conference workshop focused on incorporating prior knowledge—such as physical constraints, conservation laws, and symmetries—into machine learning models. It promotes a shift in scientific machine learning from optimizing purely for fitting accuracy toward balancing physical consistency with predictive accuracy.

Timeline (last 90 days)

Oct 12

The CL4SM (Constrained Learning for Scientific Modeling) workshop is a pre-conference event at NeurIPS 2026 in Paris, held as a track within the ELLIS UnConference.

Unverified50%
Oct 12

The core proposition of the CL4SM workshop is to promote machine learning models that respect conservation laws, symmetries, stability, and other prior knowledge.

Unverified50%
Oct 12

The CL4SM workshop accepts two types of two-page submissions (including references): work in progress, and tutorials, demos, software libraries, and datasets.

Unverified50%
Oct 12

The CL4SM workshop is non-archival, meaning submissions do not affect a paper's future publication in formal venues; it uses the PMLR template and adopts single-blind review.

Unverified50%
Oct 12

The CL4SM workshop submission deadline is October 30, 2026 (AoE 23:59), with notifications on November 12, 2026, and the workshop date on December 8, 2026.

Unverified50%
Oct 12

The CL4SM workshop venue is the Palais des Congrès de Paris, just before the official opening of NeurIPS 2026.

Unverified50%
Oct 12

The CL4SM call for papers explicitly covers application domains including climate, fluid dynamics, engineering, energy, and industry.

Unverified50%

All Facts (7)

Source Articles