Empowering metasurfaces with inverse design: principles and applications
Conventional human-driven methods face limitations in designing complex functional
metasurfaces. Inverse design is poised to empower metasurface research by embracing fast …
metasurfaces. Inverse design is poised to empower metasurface research by embracing fast …
Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport
Deep neural operators can learn operators mapping between infinite-dimensional function
spaces via deep neural networks and have become an emerging paradigm of scientific …
spaces via deep neural networks and have become an emerging paradigm of scientific …
[HTML][HTML] Inverse design enables large-scale high-performance meta-optics reshaping virtual reality
Meta-optics has achieved major breakthroughs in the past decade; however, conventional
forward design faces challenges as functionality complexity and device size scale up …
forward design faces challenges as functionality complexity and device size scale up …
Neural operator-based surrogate solver for free-form electromagnetic inverse design
Neural operators have emerged as a powerful tool for solving partial differential equations in
the context of scientific machine learning. Here, we implement and train a modified Fourier …
the context of scientific machine learning. Here, we implement and train a modified Fourier …
[HTML][HTML] Prediction of solar energetic events impacting space weather conditions
Aiming to assess the progress and current challenges on the formidable problem of the
prediction of solar energetic events since the COSPAR/International Living With a Star …
prediction of solar energetic events since the COSPAR/International Living With a Star …
Learning continuous models for continuous physics
Dynamical systems that evolve continuously over time are ubiquitous throughout science
and engineering. Machine learning (ML) provides data-driven approaches to model and …
and engineering. Machine learning (ML) provides data-driven approaches to model and …
Diffeomorphism Neural Operator for various domains and parameters of partial differential equations
Solving partial differential equations (PDEs) across varying geometric domains and
parameters represents a significant challenge in fields such as materials science …
parameters represents a significant challenge in fields such as materials science …
Physics‐Informed Machine Learning for Inverse Design of Optical Metamaterials
Optical metamaterials manipulate light through various confinement and scattering
processes, offering unique advantages like high performance, small form factor and easy …
processes, offering unique advantages like high performance, small form factor and easy …
Rapid prediction of indoor airflow field using operator neural network with small dataset
H Gao, W Qian, J Dong, J Liu - Building and Environment, 2024 - Elsevier
Indoor airflow is one of the most critical factors affecting room comfort. The accurate
prediction of indoor airflow fields is essential for efficient environmental control. However …
prediction of indoor airflow fields is essential for efficient environmental control. However …
Active Learning for Neural PDE Solvers
D Musekamp, M Kalimuthu, D Holzmüller… - arXiv preprint arXiv …, 2024 - arxiv.org
Solving partial differential equations (PDEs) is a fundamental problem in engineering and
science. While neural PDE solvers can be more efficient than established numerical solvers …
science. While neural PDE solvers can be more efficient than established numerical solvers …