367 citations · 409 across the 13 of their papers we have counts for
5 papers · 1 filter
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…
Design of Turing Systems with Physics-Informed Neural Networks
Jordon Kho, Winston Koh, Jian Cheng Wong +2
Reaction-diffusion (Turing) systems are fundamental to the formation of spatial patterns in nature and engineering. These systems are governed by a set of non-linear partial differ…
Automated Quantification of Traffic Particulate Emissions via an Image Analysis Pipeline
Kong Yuan Ho, Chin Seng Lim, Matthena A. Kattar +3
Traffic emissions are known to contribute significantly to air pollution around the world, especially in heavily urbanized cities such as Singapore. It has been previously shown th…
Robustness of Physics-Informed Neural Networks to Noise in Sensor Data
Jian Cheng Wong, Pao-Hsiung Chiu, Chin Chun Ooi +1
Physics-Informed Neural Networks (PINNs) have been shown to be an effective way of incorporating physics-based domain knowledge into neural network models for many important real-w…
FastFlow: AI for Fast Urban Wind Velocity Prediction
Shi Jer Low, Venugopalan, S. G. Raghavan +4
Data-driven approaches, including deep learning, have shown great promise as surrogate models across many domains. These extend to various areas in sustainability. An interesting d…