Hybrid modeling: towards the next level of scientific computing in engineering

• DOI: 10.1186/s13362-022-00123-0
• Publication Date: 2022-03-03T13:05:54Z
• Authors: Stefan Kurz; Herbert De Gersem; Armin Galetzka; Andreas Klaedtke; Melvin Liebsch; Dimitrios Loukrezis; Stephan Russenschuck; Manuel Schmidt
• Keywords: Physical law

Abstract The integration of machine learning (Keplerian paradigm) and more general artificial intelligence technologies with physical modeling based on first principles (Newtonian will impact scientific computing in engineering fundamental ways. Such hybrid models combine principle-based data-based into a joint architecture. This paper give some background, explain trends showcase recent achievements from an applied mathematics industrial perspective. Examples include characterization superconducting accelerator magnets by blending data physics, data-driven magnetostatic field simulation without explicit model the constitutive law, Bayesian free-shape optimization trace pair bend printed circuit board.