Toward intelligent food drying: Integrating artificial intelligence into drying systems
Artificial intelligence (AI) and its data-driven counterpart, machine learning (ML), are rapidly
evolving disciplines with increasing applications in modeling, simulation, control, and …
evolving disciplines with increasing applications in modeling, simulation, control, and …
[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 …
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 …
[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 …
Uncertainty quantification of vibro-acoustic coupling problems for robotic manta ray models based on deep learning
This study proposes a deep learning framework to perform uncertainty quantification of vibro-
acoustic coupling problems for robot manta rays. First, Loop subdivision surfaces are used …
acoustic coupling problems for robot manta rays. First, Loop subdivision surfaces are used …
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 …
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 …
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 …