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Generative diffusion models in infinite dimensions: a survey. [PDF]
Philos Trans A Math Phys Eng SciFranzese G, Michiardi P.
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Quantifying the Single-Cell Morphological Landscape of Cellular Transdifferentiation through Force Field Reconstruction. [PDF]
Adv Sci (Weinh)Yu C, Liu C, Wang E, Wang J.
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Modeling magnetic properties of cobalt nanofilms used as a component of spin hybrid superconductor-ferromagnetic structures. [PDF]
Beilstein J NanotechnolFedotov A +3 more
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Quantifying local stability and noise levels from time series in the US Western Interconnection blackout on 10th August 1996. [PDF]
Nat CommunHeßler M, Kamps O.
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Emergence of opposing arrows of time in open quantum systems. [PDF]
Sci RepGuff T, Shastry CU, Rocco A.
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The KPZ Equation of Kinetic Interface Roughening: A Variational Perspective. [PDF]
Entropy (Basel)Wio HS +5 more
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Fractional Equations and Models, 2019
The Langevin equation is connected to the Brownian motion formulated by Einstein and Smoluchowski. The Langevin equation for a free particle with mass m is given by Langevin (CR Acad Sci Paris 146:530, 1908)
Trifce Sandev, Ž. Tomovski
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The Langevin equation is connected to the Brownian motion formulated by Einstein and Smoluchowski. The Langevin equation for a free particle with mass m is given by Langevin (CR Acad Sci Paris 146:530, 1908)
Trifce Sandev, Ž. Tomovski
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Stochastic Systems with Time Delay, 2021
While you are reading this thesis, at every instant of time countless particles of the surrounding air hit your skin due to their irregular thermal motion. There are of the order of \({\sim } 10^{23}\) molecules in every liter of air [1, 2].
S. Loos
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While you are reading this thesis, at every instant of time countless particles of the surrounding air hit your skin due to their irregular thermal motion. There are of the order of \({\sim } 10^{23}\) molecules in every liter of air [1, 2].
S. Loos
semanticscholar +2 more sources
Data-Driven Learning of the Generalized Langevin Equation with State-Dependent Memory.
Physical Review Letters, 2023We present a data-driven method to learn stochastic reduced models of complex systems that retain a state-dependent memory beyond the standard generalized Langevin equation with a homogeneous kernel.
Pei Ge, Zhongqiang Zhang, Huan Lei
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