[HTML][HTML] Big data, machine learning, and digital twin assisted additive manufacturing: A review
Additive manufacturing (AM) has undergone significant development over the past decades,
resulting in vast amounts of data that carry valuable information. Numerous research studies …
resulting in vast amounts of data that carry valuable information. Numerous research studies …
[HTML][HTML] A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …
large amount of data to achieve exceptional performance. Unfortunately, many applications …
[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 …
Three-dimensional metamaterials exhibiting extreme isotropy and negative Poisson's ratio
This research will develop a new set of mechanical metamaterials with simultaneous ideal
elastic isotropy and extreme negative Poisson's ratio through a topology optimization …
elastic isotropy and extreme negative Poisson's ratio through a topology optimization …
On multiphysics concurrent multiscale topology optimization for designing porous heat-activated compliant mechanism under convection for additive manufacture
In this research, we investigated the use of concurrent multiscale topology optimization to
design additively manufacturable lightweight porous compliant mechanisms that enable …
design additively manufacturable lightweight porous compliant mechanisms that enable …
Deep-learning accelerating topology optimization of three-dimensional coolant channels for flow and heat transfer in a proton exchange membrane fuel cell
To quickly optimize the cooling performance of three-dimensional coolant channels in a
proton exchange membrane fuel (PEMFC) cell, a generative adversarial network (GAN) …
proton exchange membrane fuel (PEMFC) cell, a generative adversarial network (GAN) …
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 …
A correlation among industry 4.0, additive manufacturing, and topology optimization: A state-of-the-art review
This paper discusses additive manufacturing (AM) and topology optimization (TO) and their
relationship with industrial revolution 4.0. An overview of different AM techniques is given …
relationship with industrial revolution 4.0. An overview of different AM techniques is given …
[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 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 …