11 citations · 31 across the 8 of their papers we have counts for
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math.OC2023★ 2 cited
AONN-2: An adjoint-oriented neural network method for PDE-constrained shape optimization
Xili Wang, Pengfei Yin, Bo Zhang +1
Shape optimization has been playing an important role in a large variety of engineering applications. Existing shape optimization methods are generally mesh-dependent and therefore…
stat.ML2023★ 5 cited
Adversarial Adaptive Sampling: Unify PINN and Optimal Transport for the Approximation of PDEs
Kejun Tang, Jiayu Zhai, Xiaoliang Wan +1
Solving partial differential equations (PDEs) is a central task in scientific computing. Recently, neural network approximation of PDEs has received increasing attention due to its…
math.OC2023
AONN: An adjoint-oriented neural network method for all-at-once solutions of parametric optimal control problems
Pengfei Yin, Guangqiang Xiao, Kejun Tang +1
Parametric optimal control problems governed by partial differential equations (PDEs) are widely found in scientific and engineering applications. Traditional grid-based numerical…