62 citations · 104 across the 2 of their papers we have counts for
2 papers
physics.flu-dyn2022★ 62 cited
Residual-based physics-informed transfer learning: A hybrid method for accelerating long-term CFD simulations via deep learning
Joongoo Jeon, Juhyeong Lee, Ricardo Vinuesa +1
While a big wave of artificial intelligence (AI) has propagated to the field of computational fluid dynamics (CFD) acceleration studies, recent research has highlighted that the de…
cs.LG2021★ 42 cited
Finite volume method network for acceleration of unsteady computational fluid dynamics: non-reacting and reacting flows
Joongoo Jeon, Juhyeong Lee, Sung Joong Kim
Despite rapid improvements in the performance of central processing unit (CPU), the calculation cost of simulating chemically reacting flow using CFD remains infeasible in many cas…