Automatic differentiation and its applications in physics simulation

Liu, Jinguo; Xu, Kailai

doi:10.7498/aps.70.20210813

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…Thiele-Pfeiffer (1980) described this fossil pollen type as Tricolporopollenites wackersdorfensis. Liu (1985) assigned this pollen type to the form genus Fupingipollenites wackersdorfensis. According to Wang et al (2007), the genus was a warm temperate element growing in humid to semi-arid conditions, probably in riparian and lowland forests, persisting into the Pleistocene in eastern Eurasia (VU3, VU4).…”

Section: Polypodiopsida Spores Of Uncertain Affinitymentioning

confidence: 99%

Paleovegetation and paleoclimate inferences of the early late Sarmatian palynoflora from the Gleisdorf Fm. at Gratkorn, Styria, Austria

Geier

Bouchal

Ulrich

et al. 2022

Review of Palaeobotany and Palynology

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Section: Polypodiopsida Spores Of Uncertain Affinitymentioning

confidence: 99%

Paleovegetation and paleoclimate inferences of the early late Sarmatian palynoflora from the Gleisdorf Fm. at Gratkorn, Styria, Austria

Geier

Bouchal

Ulrich

et al. 2022

Review of Palaeobotany and Palynology

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“…In addition, an increasing number of studies in recent years have shown that in ICF, due to the presence of high energy density particles and extreme and violent changes in physical quantities, there are strong nonequilibrium kinetic effects, which are nonnegligible and have an impact on the implosion process. In other fields, nonequilibrium behavioral characteristics induced by flow behaviors such as shock waves are also receiving increasing attention [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Nonequilibrium kinetics effects in Richtmyer–Meshkov instability and reshock processes

Shan,

Xu,

Wang

et al. 2023

Commun. Theor. Phys.

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Kinetic effects in the inertial conﬁnement fusion ignition process are far from clear. In this work, we study the Richtmyer-Meshkov instability (RMI) and reshock processes by using a two-ﬂuid discrete Boltzmann method (DBM). The work begins from interpreting the experiment conducted by Collins and Jacobs [J. Fluid Mech. 464,113-136(2002)]. It shows that the shock wave causes substances in close proximity to the substance interface to deviate more signiﬁcantly from their thermodynamic equilibrium state. The Thermodynamic NonEquilibrium (TNE) quantities exhibit complex but inspiring kinetic effects in the shock process and behind the shock front. The kinetic effects are detected by two sets of TNE quantities. The ﬁrst set includes │Δ2 *│,│Δ3,1 *│,│Δ3 *│,and │Δ4,2 *│, which correspond to the intensities of the Non-Organized Momentum Flux (NOMF), Non-Organized Energy Flux (NOEF),the ﬂux of NOMF and the ﬂux of NOEF. All four TNE measures abruptly increase in the shock process. The second set of TNE quantities includes SNOMF, SNOEF and Ssum. The mixing zone is the primary contributor to SNOEF, while the ﬂow ﬁeld region outside of the mixing zone is the primary contributor to SNOMF. Additionally, each substance exhibits different behaviors in terms of entropy production rate, and the lighter ﬂuid has a higher entropy production rate than the heavier ﬂuid.

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Physics-Informed Neural Network for Flow Prediction Based on Flow Visualization in Bridge Engineering

et al. 2023

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Wind loads can endanger the safety and stability of bridges, especially long-span cable-supported bridges. Therefore, it is important to evaluate the potential wind loads during the bridge design stage. Traditionally, wind load evaluation is performed by wind tunnel testing, which is relatively expensive. With the development of computational fluid dynamics and high-performance computing, numerical simulations are becoming more accessible for designers. However, the costs required for accurate numerical results are still high, especially for high-fidelity simulations. Under this condition, searching for a more efficient method to evaluate the wind loads in bridge wind engineering has become a new goal. It seems that flow visualization is a good entry point. Although flow visualization techniques have been developed in recent years, it remains difficult to extract velocity and pressure fields from images. To address this problem, physics-informed neural networks (PINNs) have been developed and validated. This study establishes a PINN to investigate the two-dimensional viscous incompressible fluid flow passing a generic bridge deck section. Two cases with different Reynolds numbers are tested. After careful training, it is found that the PINN can accurately extract the velocity and pressure fields from the concentration field and predict the drag and lift coefficients. The results demonstrate that PINNs are a promising method for extracting useful flow information from flow visualization data in engineering applications.

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Automatic differentiation and its applications in physics simulation

Cited by 3 publications

References 28 publications

Paleovegetation and paleoclimate inferences of the early late Sarmatian palynoflora from the Gleisdorf Fm. at Gratkorn, Styria, Austria

Paleovegetation and paleoclimate inferences of the early late Sarmatian palynoflora from the Gleisdorf Fm. at Gratkorn, Styria, Austria

Nonequilibrium kinetics effects in Richtmyer–Meshkov instability and reshock processes

Physics-Informed Neural Network for Flow Prediction Based on Flow Visualization in Bridge Engineering

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