19 citations · 28 across the 6 of their papers we have counts for
5 papers · 1 filter
Towards Spatio-Temporal Extrapolation of Phase-Field Simulations with Convolution-Only Neural Networks
Christophe Bonneville, Nathan Bieberdorf, Pieterjan Robbe +4
Phase-field simulations of liquid metal dealloying (LMD) can capture complex microstructural evolutions but can be prohibitively expensive for large domains and long time horizons.…
Extrapolating Phase-Field Simulations in Space and Time with Purely Convolutional Architectures
Christophe Bonneville, Nathan Bieberdorf, Pieterjan Robbe +4
Phase-field models of liquid metal dealloying (LMD) can resolve rich microstructural dynamics but become intractable for large domains or long time horizons. We present a condition…
Accelerating Phase Field Simulations Through a Hybrid Adaptive Fourier Neural Operator with U-Net Backbone
Christophe Bonneville, Nathan Bieberdorf, Arun Hegde +4
Prolonged contact between a corrosive liquid and metal alloys can cause progressive dealloying. For such liquid-metal dealloying (LMD) process, phase field models have been develop…
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling
Christophe Bonneville, Xiaolong He, April Tran +10
Numerical solvers of partial differential equations (PDEs) have been widely employed for simulating physical systems. However, the computational cost remains a major bottleneck in…
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical Simulations
Christophe Bonneville, Youngsoo Choi, Debojyoti Ghosh +1
Traditional partial differential equation (PDE) solvers can be computationally expensive, which motivates the development of faster methods, such as reduced-order-models (ROMs). We…