94 citations · 146 across the 16 of their papers we have counts for
4 papers · 1 filter
Landscape-aware Automated Algorithm Design: An Efficient Framework for Real-world Optimization
Haoran Yin, Shuaiqun Pan, Zhao Wei +5
The advent of Large Language Models (LLMs) has opened new frontiers in automated algorithm design, giving rise to numerous powerful methods. However, these approaches retain critic…
Evolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunities
Jian Cheng Wong, Abhishek Gupta, Chin Chun Ooi +3
Deep learning models trained on finite data lack a complete understanding of the physical world. On the other hand, physics-informed neural networks (PINNs) are infused with such k…
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…
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…