Results 321 to 330 of about 217,226 (371)
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Poseidon: Efficient Foundation Models for PDEs
Neural Information Processing SystemsWe introduce Poseidon, a foundation model for learning the solution operators of PDEs. It is based on a multiscale operator transformer, with time-conditioned layer norms that enable continuous-in-time evaluations.
Maximilian Herde +6 more
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(CO)Bordisms in PDEs and quantum PDEs
Reports on Mathematical Physics, 1996zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Parameterized Physics-informed Neural Networks for Parameterized PDEs
International Conference on Machine LearningComplex physical systems are often described by partial differential equations (PDEs) that depend on parameters such as the Reynolds number in fluid mechanics.
Woojin Cho +6 more
semanticscholar +1 more source
2009
In this chapter we deal with cascades of parabolic and second-order hyperbolic PDEs. These are example problems. The parabolic-hyperbolic cascade is represented by a heat equation at the input of an antistable wave equation. The hyperbolic-parabolic cascade is represented by a wave equation at the input of an unstable reaction-diffusion equation.
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In this chapter we deal with cascades of parabolic and second-order hyperbolic PDEs. These are example problems. The parabolic-hyperbolic cascade is represented by a heat equation at the input of an antistable wave equation. The hyperbolic-parabolic cascade is represented by a wave equation at the input of an unstable reaction-diffusion equation.
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Phosphodiesterases (PDEs) and PDE inhibitors for treatment of LUTS
Neurourology and Urodynamics, 2007Lower urinary tract (LUT) smooth muscle can be relaxed by drugs that increase intracellular concentrations of cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP). Both of these substances are degraded by phosphodiesterases (PDEs), which play a central role in the regulation of smooth muscle tone.
Karl-Erik, Andersson +3 more
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2020
Summary: In this paper we study a number of nonlinear fractional equations, involving Caputo derivative in space or/and in time, admitting explicit solution in separating variable form. Some of these equations are particularly interesting because they admit completely periodic solutions. When time-fractional derivatives are introduced, this property is
Riccardo Droghei, Roberto Garra
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Summary: In this paper we study a number of nonlinear fractional equations, involving Caputo derivative in space or/and in time, admitting explicit solution in separating variable form. Some of these equations are particularly interesting because they admit completely periodic solutions. When time-fractional derivatives are introduced, this property is
Riccardo Droghei, Roberto Garra
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Proceedings of the 2017 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, 2017
In this paper, we present initial experiences implementing a general Parallel Discrete Event Simulation (PDES) accelerator on a Field Programmable Gate Array (FPGA). The accelerator can be specialized to any particular simulation model by defining the object states and the event handling logic, which are then synthesized into a custom accelerator for ...
Shafiur Rahman +2 more
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In this paper, we present initial experiences implementing a general Parallel Discrete Event Simulation (PDES) accelerator on a Field Programmable Gate Array (FPGA). The accelerator can be specialized to any particular simulation model by defining the object states and the event handling logic, which are then synthesized into a custom accelerator for ...
Shafiur Rahman +2 more
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2014
We first review the linear Laplace equation. For functions $$\phi\;:\;\mathbb{R}^n\;\rightarrow\;\mathbb{R}$$ we define the Lagrangian $$ L^e(\phi)\;=\;\frac{1} {2}\int_{\mathbb{R}^{n}} {\left| {\nabla _x \phi } \right|^2 \,dx} \, = \,\frac{1} {2}\int_{\mathbb{R}^{n}} {\partial _\alpha \phi } \cdot \partial _\alpha \phi \,dx ,$$ with the ...
Herbert Koch +2 more
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We first review the linear Laplace equation. For functions $$\phi\;:\;\mathbb{R}^n\;\rightarrow\;\mathbb{R}$$ we define the Lagrangian $$ L^e(\phi)\;=\;\frac{1} {2}\int_{\mathbb{R}^{n}} {\left| {\nabla _x \phi } \right|^2 \,dx} \, = \,\frac{1} {2}\int_{\mathbb{R}^{n}} {\partial _\alpha \phi } \cdot \partial _\alpha \phi \,dx ,$$ with the ...
Herbert Koch +2 more
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A Deep Fourier Residual method for solving PDEs using Neural Networks
Computer Methods in Applied Mechanics and Engineering, 2023Jamie M Taylor +2 more
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