Zobrazeno 1 - 10
of 245
pro vyhledávání: '"68Q10"'
By using a formulation of a class of compressible viscous flows with a heat source via vorticity and expansion-rate, we study the Oberbeck-Boussinesq flows. To this end we establish a new integral representation for solutions of parabolic equations s
Externí odkaz:
http://arxiv.org/abs/2410.02923
Autor:
Guo, Zihao, Qian, Zhongmin
We develop a numerical method for simulation of incompressible viscous flows by integrating the technology of random vortex method with the core idea of Large Eddy Simulation (LES). Specifically, we utilize the filtering method in LES, interpreted as
Externí odkaz:
http://arxiv.org/abs/2410.00605
Plane-walking automata were introduced by Salo & T\"orma to recognise languages of two-dimensional infinite words (subshifts), the counterpart of $4$-way finite automata for two-dimensional finite words. We extend the model to allow for nondeterminis
Externí odkaz:
http://arxiv.org/abs/2409.08024
The well-studied red-blue pebble game models the execution of an arbitrary computational DAG by a single processor over a two-level memory hierarchy. We present a natural generalization to a multiprocessor setting where each processor has its own lim
Externí odkaz:
http://arxiv.org/abs/2409.03898
In the present work, we further study the computational power of virus machines (VMs in short). VMs provide a computing paradigm inspired by the transmission and replication networks of viruses. VMs consist of process units (called hosts) structured
Externí odkaz:
http://arxiv.org/abs/2409.03327
We study the problem \emph{Gathering} for $n$ autonomous mobile robots in synchronous settings with a persistent memory called \emph{light}. It is well known that Gathering is impossible in the basic model ($OBLOT$) where robots have no lights, even
Externí odkaz:
http://arxiv.org/abs/2408.09999
In this paper we introduce a novel Neural Networks-based approach for approximating solutions to the (2D) incompressible Navier--Stokes equations. Our algorithm uses a Physics-informed Neural Network, that approximates the vorticity based on a loss f
Externí odkaz:
http://arxiv.org/abs/2405.13691
Publikováno v:
In LICS'24: Proceedings of the 39th Annual ACM/IEEE Symposium on Logic in Computer Science, ACM, 2024, Article No.: 2, Pages 1-13
We propose a local, past-oriented fragment of propositional dynamic logic to reason about concurrent scenarios modelled as Mazurkiewicz traces, and prove it to be expressively complete with respect to regular trace languages. Because of locality, spe
Externí odkaz:
http://arxiv.org/abs/2405.11308
Functional integral representations for solutions of the motion equations for wall-bounded incompressible viscous flows, expressed (implicitly) in terms of distributions of solutions to stochastic differential equations of McKean-Vlasov type, are est
Externí odkaz:
http://arxiv.org/abs/2403.15549
Artificial neural networks (ANNs) are highly flexible predictive models. However, reliably quantifying uncertainty for their predictions is a continuing challenge. There has been much recent work on "recalibration" of predictive distributions for ANN
Externí odkaz:
http://arxiv.org/abs/2403.05756