[HTML][HTML] Fundamental understanding of heat and mass transfer processes for physics-informed machine learning-based drying modelling
Drying is a complex process of simultaneous heat, mass, and momentum transport
phenomena with continuous phase changes. Numerical modelling is one of the most …
phenomena with continuous phase changes. Numerical modelling is one of the most …
A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics
Despite its rapid development, Physics-Informed Neural Network (PINN)-based
computational solid mechanics is still in its infancy. In PINN, the loss function plays a critical …
computational solid mechanics is still in its infancy. In PINN, the loss function plays a critical …
[HTML][HTML] Recent advances in determining the cellular-level property evolutions of plant-based food materials during drying
Background Determination of the changes in cellular-level structural, mechanical,
rheological and transport properties during the processing of plant-based food materials …
rheological and transport properties during the processing of plant-based food materials …
A physics-informed neural network-based topology optimization (PINNTO) framework for structural optimization
Abstract Physics-Informed Neural Networks (PINNs) have recently attracted exponentially
increasing attention in the field of computational mechanics. This paper proposes a novel …
increasing attention in the field of computational mechanics. This paper proposes a novel …
[HTML][HTML] A complete physics-informed neural network-based framework for structural topology optimization
Abstract Physics-Informed Neural Networks (PINNs) have recently gained increasing
attention in the field of topology optimization. The fusion of deep learning and topology …
attention in the field of topology optimization. The fusion of deep learning and topology …
Physics-informed deep neural network for modeling the chloride diffusion in concrete
Chloride diffusion in concrete is a complex chemo-physical process and it is of pivotal
importance to forecast the initiation time of corrosion. But limited equations are accessible to …
importance to forecast the initiation time of corrosion. But limited equations are accessible to …
The application of physics-informed machine learning in multiphysics modeling in chemical engineering
Physics-Informed Machine Learning (PIML) is an emerging computing paradigm that offers a
new approach to tackle multiphysics modeling problems prevalent in the field of chemical …
new approach to tackle multiphysics modeling problems prevalent in the field of chemical …
[HTML][HTML] Physics-informed radial basis network (PIRBN): A local approximating neural network for solving nonlinear partial differential equations
Our recent study has found that physics-informed neural networks (PINN) tend to be local
approximators after training. This observation led to the development of a novel physics …
approximators after training. This observation led to the development of a novel physics …
A general Neural Particle Method for hydrodynamics modeling
Abstract Neural Particle Method (NPM) is a newly proposed Physics-Informed Neural
Network (PINN) based, truly meshfree method for hydrodynamics modeling. In the NPM …
Network (PINN) based, truly meshfree method for hydrodynamics modeling. In the NPM …
A novel physics-informed neural networks approach (PINN-MT) to solve mass transfer in plant cells during drying
CP Batuwatta-Gamage, C Rathnayaka… - Biosystems …, 2023 - Elsevier
Predicting microscale mechanisms of plant-based food materials has been an enduring
challenge due to the inherent complexity of involved physics and prohibitively-high …
challenge due to the inherent complexity of involved physics and prohibitively-high …