Results 21 to 30 of about 11,256,803 (209)
Physics-informed learning of governing equations from scarce data [PDF]
Nature Communications, 2020 Harnessing data to discover the underlying governing laws or equations that describe the behavior of complex physical systems can significantly advance our modeling, simulation and understanding of such systems in various science and engineering ...
Zhao Chen, Yang Liu, Hao Sun
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The Journal of the Learning Sciences, 2020 Background There is widespread agreement that participation in post-compulsory physics needs to be widened and increased, particularly among women and under-represented communities.
L. Archer, J. Moote, E. MacLeod
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Physics-informed neural networks for phase-field method in two-phase flow
The Physics of Fluids, 2022 The complex flow modeling based on machine learning is becoming a promising way to describe multiphase fluid systems. This work demonstrates how a physics-informed neural network promotes the combination of traditional governing equations and advanced ...
Rundi Qiu +7 more
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Intelligent Reflecting Surfaces: Physics, Propagation, and Pathloss Modeling [PDF]
IEEE Wireless Communications Letters, 2019 Intelligent reflecting surfaces can improve the communication between a source and a destination. The surface contains metamaterial that is configured to “reflect” the incident wave from the source towards the destination.
Özgecan Özdogan +2 more
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The Gravitational-wave physics II: Progress [PDF]
Science China Physics Mechanics and Astronomy, 2021 It has been a half-decade since the first direct detection of gravitational waves, which signifies the coming of the era of the gravitational-wave astronomy and gravitational-wave cosmology. The increasing number of the detected gravitational-wave events
Ligong Bian +17 more
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TossingBot: Learning to Throw Arbitrary Objects With Residual Physics [PDF]
IEEE Transactions on robotics, 2019 We investigate whether a robot arm can learn to pick and throw arbitrary rigid objects into selected boxes quickly and accurately. Throwing has the potential to increase the physical reachability and picking speed of a robot arm.
Andy Zeng +4 more
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Super-resolution and denoising of fluid flow using physics-informed convolutional neural networks without high-resolution labels [PDF]
The Physics of Fluids, 2020 High-resolution (HR) information of fluid flows, although preferable, is usually less accessible due to limited computational or experimental resources. In many cases, fluid data are generally sparse, incomplete, and possibly noisy.
Han Gao, Luning Sun, Jian-Xun Wang
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Stiff-PINN: Physics-Informed Neural Network for Stiff Chemical Kinetics [PDF]
AAAI Spring Symposium: MLPS, 2020 The recently developed physics-informed neural network (PINN) has achieved success in many science and engineering disciplines by encoding physics laws into the loss functions of the neural network such that the network not only conforms to the ...
Weiqi Ji +4 more
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A framework for improving diversity work in physics
, 2020 In this paper we draw on Black Feminist Theory to motivate examining diversity work in physics with an emphasis on physics education research. In our framework, we consider three major approaches to doing diversity work acknowledging and naming diversity,
Geraldine Cochran, Mildred Boveda
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Fast inference of deep neural networks in FPGAs for particle physics [PDF]
Journal of Instrumentation, 2018 Recent results at the Large Hadron Collider (LHC) have pointed to enhanced physics capabilities through the improvement of the real-time event processing techniques.
Javier Mauricio Duarte +10 more
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