Search Results for author:
Found 9 papers, 1 papers with code
Generalizable Neural Physics Solvers by Baldwinian Evolution
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 laws and able to accurately simulate them.
Graph Neural Network Based Surrogate Model of Physics Simulations for Geometry Design
Computational Intelligence (CI) techniques have shown great potential as a surrogate model of expensive physics simulation, with demonstrated ability to make fast predictions, albeit at the expense of accuracy in some cases.
Automated Quantification of Traffic Particulate Emissions via an Image Analysis Pipeline
Traffic emissions are known to contribute significantly to air pollution around the world, especially in heavily urbanized cities such as Singapore.
Design of Turing Systems with Physics-Informed Neural Networks
Thus, we investigate the use of physics-informed neural networks as a tool to infer key parameters in reaction-diffusion systems in the steady-state for scientific discovery or design.
Robustness of Physics-Informed Neural Networks to Noise in Sensor Data
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-world systems.
FastFlow: AI for Fast Urban Wind Velocity Prediction
Hence, it can be beneficial to urban planners to have a fast surrogate model to predict urban characteristics of a hypothetical layout, e. g. pedestrian-level wind velocity, without having to run computationally expensive and time-consuming high-fidelity numerical simulations.
Model-Agnostic Hybrid Numerical Weather Prediction and Machine Learning Paradigm for Solar Forecasting in the Tropics
Weather Research and Forecasting (WRF) model is run in both global and regional mode to provide an estimate for solar irradiance.
U-Net-Based Surrogate Model For Evaluation of Microfluidic Channels
The surrogate model is fast, easy to set-up and can be used to predict and assess the flow velocity and pressure fields across the domain for new designs of interest via the input of a geometry-encoding matrix.
Surrogate Modeling of Fluid Dynamics with a Multigrid Inspired Neural Network Architecture
Algebraic or geometric multigrid methods are commonly used in numerical solvers as they are a multi-resolution method able to handle problems with multiple scales.
