arXiv:2503.01684v3 Announce Type: replace-cross Abstract: Constructing fast and accurate surrogate models is a key ingredient for making robust predictions in many topics. We introduce a new model, the Multiparameter Eigenvalue Problem (MEP) emulator. The new method connects emulators and can make predictions directly from observables to observables. We present that the MEP emulator can be trained with data from Eigenvector Continuation (EC) and Parametric Matrix Model (PMM) emulators. A simple simulation on a one-dimensional lattice confirms the performance of the MEP emulator. Using $^{28}$O
The continuous need for efficient and accurate surrogate models in complex scientific fields, coupled with advances in AI learning methods, drives the development of tools like the MEP emulator.
This development can significantly accelerate scientific discovery and engineering design by providing faster, more robust predictions, reducing the need for computationally expensive simulations.
The ability to directly connect observables through efficient learning methods will streamline the modeling process, allowing for more rapid iteration and better understanding of complex systems.
- · AI researchers
- · Computational physicists
- · Engineering R&D
- · Materials science
- · Traditional simulation-heavy R&D
- · Inefficient modeling approaches
Faster development cycles for new materials and scientific applications will become possible.
Reduced computational costs in research could democratize access to advanced modeling techniques.
The proliferation of efficient surrogates may lead to AI-driven autonomous experimental design loops, accelerating discovery further.
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Read at arXiv cs.LG