Courant: a State-Adaptive Perceiver-Based Neural Surrogate with Local Support and Interpretable Field Decomposition
arXiv:2605.25115v1 Announce Type: new Abstract: We introduce "Courant", a Perceiver-based encoder-processor-decoder surrogate model that has latent features exhibiting adaptive specialization and local support in the physical space, enabling functionality akin to an adaptive hp-refinement scheme, an attribute that is highly desirable in traditional numerical solvers and scientific machine learning broadly. The proposed architecture combines a shared random Fourier feature coordinate embedding, state-adapted latent queries, and a light-weight decoder. Courant is trained end-to-end with steady o
The continuous advancements in AI and scientific machine learning are pushing for more efficient and interpretable surrogate models for complex physical simulations.
This development represents a significant step towards more accurate, computationally efficient, and interpretable AI models for scientific and engineering applications, potentially accelerating research and development cycles.
Traditional numerical solvers and scientific machine learning models gain a novel, adaptive, and interpretable neural surrogate that can handle complex physical phenomena with enhanced precision and localized support.
- · Scientific machine learning researchers
- · Engineering simulation software developers
- · AI hardware manufacturers
- · Computational fluid dynamics sector
- · Traditional, computationally intensive simulation methods
- · Organizations reliant solely on older numerical solvers
Courant enables faster and more accurate simulations in various scientific and engineering disciplines.
Accelerated design and optimization cycles for complex systems, from aerospace to materials science, become possible.
Reduced time-to-market for innovative products and a shift in research methodology toward AI-driven simulation and discovery.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG