Results 11 to 20 of about 2,291 (214)

Meta-learning PINN loss functions [PDF]

Journal of Computational Physics, 2022
We propose a meta-learning technique for offline discovery of physics-informed neural network (PINN) loss functions. We extend earlier works on meta-learning, and develop a gradient-based meta-learning algorithm for addressing diverse task distributions based on parametrized partial differential equations (PDEs) that are solved with PINNs. Furthermore,
Apostolos F. Psaros, Kenji Kawaguchi, George Em Karniadakis   +2 more
openaire   +3 more sources

Failure-Informed Adaptive Sampling for PINNs

SIAM Journal on Scientific Computing, 2023
Physics-informed neural networks (PINNs) have emerged as an effective technique for solving PDEs in a wide range of domains. It is noticed, however, the performance of PINNs can vary dramatically with different sampling procedures. For instance, a fixed set of (prior chosen) training points may fail to capture the effective solution region (especially ...
Zhiwei Gao, Liang Yan 0002, Tao Zhou 0010   +2 more
openaire   +3 more sources

Physics-informed Neural Networks:Recent Advances and Prospects [PDF]

Jisuanji kexue, 2022
Physical-informed neural networks (PINN) are a class of neural networks used to solve supervised learning tasks.They not only try to follow the distribution law of the training data, but also follow the physical laws described by partial diffe-rential ...
LI Ye, CHEN Song-can
doaj   +1 more source

Sinogram-based flow estimation in computed tomography using a physics-informed neural network: Impact of gantry rotation speed, X-ray fluence and pulsed acquisition on accuracy. [PDF]

Med Phys
Abstract Background Non‐invasive imaging‐based assessment of blood flow plays a critical role in evaluating heart function and structure. Computed Tomography (CT) is a widely‐used imaging modality that can robustly evaluate cardiovascular anatomy and function, but direct methods to estimate blood flow velocity from movies of contrast dynamics have not ...
Guo J, Khurana GS, Grande AG, Alamo JCD, Contijoch F.   +4 more
europepmc   +2 more sources

MC-Nonlocal-PINNs: Handling Nonlocal Operators in PINNs via Monte Carlo Sampling

Numerical Mathematics: Theory, Methods and Applications, 2023
We propose Monte Carlo Nonlocal physics-informed neural networks (MC-Nonlocal-PINNs), which are a generalization of MC-fPINNs in L. Guo et al. (Comput. Methods Appl. Mech. Eng. 400 (2022), 115523) for solving general nonlocal models such as integral equations and nonlocal PDEs.
Xiaodong Feng, Yue Qian, Wanfang Shen
openaire   +2 more sources

Solving partial differential equations with hybridized physic-informed neural network and optimization approach: Incorporating genetic algorithms and L-BFGS for improved accuracy

Alexandria Engineering Journal, 2023
Partial differential equations (PDEs) are essential mathematical models for describing a wide range of physical phenomena. Numerically, Physic-Informed Neural Networks (PINNs), a variant of artificial neural networks, present a promising method for ...
Danang A. Pratama, Rewayda Razaq Abo-Alsabeh, Maharani A. Bakar, A. Salhi, Nur Fadhilah Ibrahim   +4 more
doaj   +1 more source

High content screening miniaturization and single cell imaging of mature human feeder layer-free iPSC-derived neurons

SLAS Discovery, 2023
Human induced pluripotent stem cell (iPSC)-derived neurons are being increasingly used for high content imaging and screening. However, iPSC-derived neuronal differentiation and maturation is time-intensive, often requiring >8 weeks.
Elizabeth R. Sharlow, Danielle C. Llaneza, William E. Grever, Garnett A. Mingledorff, Anna J. Mendelson, George S. Bloom, John S. Lazo   +6 more
doaj   +1 more source

Transfer learning-enhanced physics informed neural network for accurate melt pool prediction in laser melting [PDF]

Advanced Manufacturing
The profile of the melt pool is essential in selective laser melting (SLM) to control the process quality and avoid defects. Physics informed neural network (PINN) method is proposed to address challenges in various science and engineering problems when ...
Qingyun Zhu, Zhengxin Lu, Yaowu Hu
doaj   +1 more source

Solving partial differential equation for atmospheric dispersion of radioactive material using physics-informed neural network

Nuclear Engineering and Technology, 2023
The governing equations of atmospheric dispersion most often taking the form of a second-order partial differential equation (PDE). Currently, typical computational codes for predicting atmospheric dispersion use the Gaussian plume model that is an ...
Gibeom Kim, Gyunyoung Heo
doaj   +1 more source

Transfer Learning-Based Coupling of Smoothed Finite Element Method and Physics-Informed Neural Network for Solving Elastoplastic Inverse Problems

Mathematics, 2023
In practical engineering applications, there is a high demand for inverting parameters for various materials, and obtaining monitoring data can be costly.
Meijun Zhou, Gang Mei
doaj   +1 more source