367 citations · 409 across the 13 of their papers we have counts for
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cs.NE2023★ 1 cited
Evolutionary Optimization of Physics-Informed Neural Networks: Advancing Generalizability by the Baldwin Effect
Jian Cheng Wong, Chin Chun Ooi, Abhishek Gupta +4
Physics-informed neural networks (PINNs) are at the forefront of scientific machine learning, making possible the creation of machine intelligence that is cognizant of physical law…
cs.NE2022★ 4 cited
Neuroevolution of Physics-Informed Neural Nets: Benchmark Problems and Comparative Results
Nicholas Sung Wei Yong, Jian Cheng Wong, Pao-Hsiung Chiu +3
The potential of learned models for fundamental scientific research and discovery is drawing increasing attention worldwide. Physics-informed neural networks (PINNs), where the los…