arXiv subjects

Aleksey Panas

Publications and source records attributed to Aleksey Panas.

2 recordsLinked to original sources

arXiv2026

Learning Lagrangian Interaction Dynamics with Sampling-Based Model Order Reduction

Simulating physical systems governed by Lagrangian dynamics often entails solving partial differential equations (PDEs) over high-resolution spatial domains, leading to significant computational expense. Reduced-order modeling (ROM) mitigates this cost by evolving low-dimensional latent representations of the underlying system. While neural ROMs enable querying solutions from latent states at arbitrary spatial points, their latent states typically represent the global domain and struggle to capture localized, highly dynamic behaviors such as fluids. We propose a sampling-based reduction framework that evolves Lagrangian systems directly in physical space over the particles themselves, reducing the number of active degrees of freedom via data-driven neural PDE operators. To enable querying at arbitrary spatial locations, we introduce a learnable kernel parameterization that uses local spatial information from time-evolved sample particles to infer the underlying solution manifold. Empirically, our approach achieves a 6.6x to 32x reduction in input dimensionality while maintaining high-fidelity evaluations across diverse Lagrangian regimes, including fluid flows, granular media, and elastoplastic dynamics. We refer to this framework as GIOROM (Geometry-Informed Reduced-Order Modeling). All code and data are available at: https://github.com/HrishikeshVish/GIOROM

cs.LG↗

arXiv2024

Towards Reconfigurable Linearizable Reads

Linearizable datastores are desirable because they provide users with the illusion that the datastore is run on a single machine that performs client operations one at a time. To reduce the performance cost of providing this illusion, many specialized algorithms for linearizable reads have been proposed which significantly improve read performance compared to write performance. The main difference between these specialized algorithms is their performance under different workloads. Unfortunately, since a datastore's workload is often unknown or changes over time and system designers must decide on a single read algorithm to implement ahead of time, a datastore's performance is often suboptimal as it cannot adapt to workload changes. In this paper, we lay the groundwork for addressing this problem by proposing Chameleon, an algorithm for linearizable reads that provides a principled approach for datastores to switch between existing read algorithms at runtime. The key observation that enables this generalization is that all existing algorithms are specific read-write quorum systems. Chameleon constructs a generic read-write quorum system, by using tokens that are included to complete write and read operations. This token quorum system enables Chameleon to mimic existing read algorithms and switch between them by transferring these tokens between processes.

cs.DC↗