Abstract: Physics-informed neural networks (PINNs) are a novel approach to solving partial differential equations (PDEs) through deep learning. They offer a unified manner for solving forward and inverse problems, which is beneficial ...

Abstract: Sensors play an important role in monitoring manufacturing processes and update their digital twins. However, the data transmission bandwidth and sensor placement limitations in the physical systems may not allow us to ...

Abstract: When neural networks are applied to solve complex engineering problems, the lack of training data can make the predictions of the surrogate inaccurate. Recently, physics-constrained neural networks were introduced to ...

Abstract: Fluid dynamics is governed by partial differential equations (PDEs) which are solved numerically. The limitations of traditional methods in data assimilation hinder their effective engagement with experiments. Physics-informed ...

Abstract: Fluid dynamics is governed by partial differential equations (PDEs) which are solved numerically. The limitations of traditional methods in data assimilation hinder their effective engagement with experiments. Physics-informed ...

Abstract: When neural networks are applied to solve complex engineering problems, the lack of training data can make the predictions of the surrogate inaccurate. Recently, physicsconstrained neural networks were introduced to integrate ...

Abstract: Abstract. Machine learning (ML) technique is a critical tool to promote optimal design and ensure reliable and efficient products and processes in the manufacturing industry, since it can discover hidden knowledge and build ...

Abstract: Generative artificial intelligence (AI) refers to the domain of AI systems designed to generate new information and artifacts by sampling from complex distributions captured from the data they were trained on. Encompassing ...