A review on guided-ultrasonic-wave-based structural health monitoring: From fundamental theory to machine learning techniques
The development of structural health monitoring (SHM) techniques is of great importance to
improve the structural efficiency and safety. With advantages of long propagation distances …
improve the structural efficiency and safety. With advantages of long propagation distances …
On neural differential equations
P Kidger - arxiv preprint arxiv:2202.02435, 2022 - arxiv.org
The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …
Pde-refiner: Achieving accurate long rollouts with neural pde solvers
Time-dependent partial differential equations (PDEs) are ubiquitous in science and
engineering. Recently, mostly due to the high computational cost of traditional solution …
engineering. Recently, mostly due to the high computational cost of traditional solution …
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
One of the most promising applications of machine learning in computational physics is to
accelerate the solution of partial differential equations (PDEs). The key objective of machine …
accelerate the solution of partial differential equations (PDEs). The key objective of machine …
Physics-informed machine learning: A survey on problems, methods and applications
Recent advances of data-driven machine learning have revolutionized fields like computer
vision, reinforcement learning, and many scientific and engineering domains. In many real …
vision, reinforcement learning, and many scientific and engineering domains. In many real …
[HTML][HTML] Advancing arctic sea ice remote sensing with ai and deep learning: opportunities and challenges
Revolutionary advances in artificial intelligence (AI) in the past decade have brought
transformative innovation across science and engineering disciplines. In the field of Arctic …
transformative innovation across science and engineering disciplines. In the field of Arctic …
Clifford neural layers for pde modeling
Partial differential equations (PDEs) see widespread use in sciences and engineering to
describe simulation of physical processes as scalar and vector fields interacting and …
describe simulation of physical processes as scalar and vector fields interacting and …
On recurrent neural networks for learning-based control: recent results and ideas for future developments
This paper aims to discuss and analyze the potentialities of Recurrent Neural Networks
(RNN) in control design applications. The main families of RNN are considered, namely …
(RNN) in control design applications. The main families of RNN are considered, namely …
Lie point symmetry data augmentation for neural PDE solvers
Neural networks are increasingly being used to solve partial differential equations (PDEs),
replacing slower numerical solvers. However, a critical issue is that neural PDE solvers …
replacing slower numerical solvers. However, a critical issue is that neural PDE solvers …
A posteriori learning for quasi‐geostrophic turbulence parametrization
The use of machine learning to build subgrid parametrizations for climate models is
receiving growing attention. State‐of‐the‐art strategies address the problem as a supervised …
receiving growing attention. State‐of‐the‐art strategies address the problem as a supervised …