Conference
Enforcing governing equation constraints in neural PDE solvers via training-free projections
| Τίτλος: | Enforcing governing equation constraints in neural PDE solvers via training-free projections |
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| Συγγραφείς: | Rochman Sharabi, Omer, Louppe, Gilles |
| Πηγή: | Machine Learning and the Physical Sciences Workshop (NeurIPS 2025), San Diego, United States - California [US-CA], 06/12/2025 |
| Έτος έκδοσης: | 2025 |
| Θεματικοί όροι: | Computer Science - Learning, Engineering, computing & technology, Computer science, Ingénierie, informatique & technologie, Sciences informatiques |
| Περιγραφή: | Neural PDE solvers used for scientific simulation often violate governing equation constraints. While linear constraints can be projected cheaply, many constraints are nonlinear, complicating projection onto the feasible set. Dynamical PDEs are especially difficult because constraints induce long-range dependencies in time. In this work, we evaluate two training-free, post hoc projections of approximate solutions: a nonlinear optimization-based projection, and a local linearization-based projection using Jacobian-vector and vector-Jacobian products. We analyze constraints across representative PDEs and find that both projections substantially reduce violations and improve accuracy over physics-informed baselines. |
| Τύπος εγγράφου: | conference poster not in proceedings http://purl.org/coar/resource_type/c_18co conferencePoster peer reviewed |
| Γλώσσα: | English |
| Relation: | https://arxiv.org/abs/2511.17258 |
| DOI: | 10.48550/arXiv.2511.17258 |
| Σύνδεσμος πρόσβασης: | https://orbi.uliege.be/handle/2268/340544 |
| Rights: | open access http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess |
| Αριθμός Καταχώρησης: | edsorb.340544 |
| Βάση Δεδομένων: | ORBi |
| DOI: | 10.48550/arXiv.2511.17258 |
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