ELLIs UnConference Introduces CL4SM: Constrained Learning for Scientific Modeling and Physical Consistency
By Mr.Xu Community Post
Published:
Summary:The ELLIs UnConference will host a session called CL4SM (Constrained Learning for Scientific Modelling) before the NeurIPS Paris conference, focusing on ensuring physical consistency in machine learning models for scientific and engineering systems. Topics include soft and hard constraint enforcement, physics-based constraint design, stability and safety guarantees, and applications in climate, fluid dynamics, engineering, and energy. CL4SM invites submissions of ongoing work, tutorials, demos,
Background
The ELLIs UnConference is a pre-event for the NeurIPS Paris conference, providing a forum for machine learning researchers to exchange ideas and discuss emerging topics. This year, it will host the CL4SM (Constrained Learning for Scientific Modelling) session, focusing on ensuring physical consistency in machine learning models for scientific and engineering systems.
Key Topics
The core topics of CL4SM include:
- Soft Constraint Enforcement: Implementing constraints through penalty terms and augmented loss functions.
- Hard Constraint Design: Ensuring models adhere to physical laws through structured architectural design.
- Physics-Based Constraints: Designing constraints based on governing equations, symmetries, and boundary conditions.
- Stability and Safety: Ensuring the stability and safety of learnt dynamical models.
- Probabilistic Constraints: Maintaining physical consistency under uncertainty.
- Application Domains: Applications in climate science, fluid dynamics, engineering, and energy management.
Submission Categories
The conference welcomes submissions in the following categories:
- Ongoing Work: New methods, theories, benchmarks, or applications, including early-stage ideas and negative results (talks and posters).
- Tutorials, Demos, Software Libraries, and Datasets: Presented through lightning talks and live demonstrations.
- Recently Published Papers: Submissions from top-tier conferences and journals such as JMLR, NeurIPS, ICML, and ICLR, with the original venue stated and the published PDF uploaded (talks and posters).
Important Dates
- Submission Deadline: October 30, 2026 (AoE)
- Notification: November 12, 2026
- Final Programme: November 27, 2026
- Session: December 8, 2026, Palais des Congrès de Paris (just before NeurIPS 2026).
Technical Value and Impact
The CL4SM session is significant as it addresses the critical challenge of ensuring physical consistency in machine learning models for scientific and engineering applications. By incorporating constraints, models can better reflect the physical laws of the real world, thereby enhancing their reliability and safety in practical applications. This advancement is particularly important for fields such as climate modeling, engineering design, and energy management, where the consequences of model errors can be severe.
— END —Source: Reddit r/MachineLearning (2026-10-12)
Tags: #Machine Learning #Scientific Modeling #Constrained Learning #Physical Consistency #NeurIPS
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