FEA-PINN combines a physics-informed neural network with corrective finite element simulations to predict melt pool dynamics in laser powder bed fusion at accuracy comparable to full FEA but lower computational cost.
Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Physics-Informed Machine Learning Regulated by Finite Element Analysis for Simulation Acceleration of Melt Pool Dynamics in Laser Powder Bed Fusion
FEA-PINN combines a physics-informed neural network with corrective finite element simulations to predict melt pool dynamics in laser powder bed fusion at accuracy comparable to full FEA but lower computational cost.