Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence

Proposal for requirements on industrial AI solutions

“…It is interesting to note that some of the presented requirements correspond with identified requirements on Industrial AI correspond to research fields recently identified for Scientific Machine Learning [33]. We foresee future developments of Industrial AI in the areas of i) production system autonomy [18], ii) product life cycle management [15], especially because AI life cycles will become more complex to manage [23], iii) virtual industrial assistants [34], iv) explainable AI [35] and v) the seamless fusion of different data pools in production sites.…”

Deep learning: a new tool for photonic nanostructure design

“…In recent years, a signicant amount of effort has been directed towards the use of DL to accelerate partial differential equation (PDE) solvers. 3,83 A particular attraction is that DL based PDE solvers may also be able to solve inverse problems without the need for extra effort. 83 Trivedi and co-workers 84 reported the acceleration of the nite difference frequency domain (FDFD) simulation of Maxwell's equations using data-driven models.…”

“…The last decade has witnessed a revolutionary development in the form of Deep Learning (DL), 1,2 a data-driven technique that uses a hierarchical composition of simple nonlinear modules. The broad popularity of data-driven techniques like DL has led to the development of Scientic Machine Learning (SciML), 3 a eld that aims to rene and apply data-driven techniques to tackle challenging problems in science and engineering. 4 Noteworthy uses of data-driven tools include the identication of energy materials [5][6][7][8] by accelerating searches 9 and the prediction of the results of quantum simulations.…”

On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs

“…A notable advantage of using neural networks is its efficient implementation using a dedicated hardware (see [8,22]). Such techniques have also been applied in solving partial differential equations (PDEs) [4,10,22,23,32,35], and it has become a new sub-field under the name of Scientific Machine Learning (SciML) [2,26]. The term Physics-Informed Neural Networks (PINNs) was introduced in [32] and it has become one of the most popular deep learning methods in SciML.…”