ROM
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Related Articles from SNS
New rom-com brings Arnhem Land love story to Sydney Film Festival screen
Maŋutji (Catching Eyes), a short film shot in the Arnhem Land community of Yirrkala, is making its debut at the Sydney Film Festival. The romantic comedy stars model Cindy Rostron and first-time actor Denzel Marika, who play love interests Muthali and Rakay. Mangutji is one of five films screening at the festival today that were created after a nation-wide call-out for First Nations stories about love.
"\^{I}n\c{t}elegi Rom\^ane\c{s}te?'' A Recipe for Romanian Vision-Language Models
arXiv:2605.31401v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) largely follow the text-only LLM trajectory, excelling on English benchmarks but sharply degrading on low-resource languages, where neither large-scale image-text corpora nor culturally grounded evaluations exist. We present a systematic study of building a language-specific VLM for Romanian, covering the full pipeline from data construction to architectural choices. We translate established English VLM...
"In\^{t}elegi Rom\^ane\c{s}te?'' A Recipe for Romanian Vision-Language Models
arXiv:2605.31401v1 Announce Type: new Abstract: Vision-Language Models (VLMs) largely follow the text-only LLM trajectory, excelling on English benchmarks but sharply degrading on low-resource languages, where neither large-scale image-text corpora nor culturally grounded evaluations exist. We present a systematic study of building a language-specific VLM for Romanian, covering the full pipeline from data construction to architectural choices. We translate established English VLM training...
Parametric Reduced Order Models for the Generalized Kuramoto--Sivashinsky Equations
arXiv:2502.02718v2 Announce Type: replace Abstract: The paper studies parametric Reduced Order Models (ROMs) for the Kuramoto--Sivashinsky (KS) and generalized Kuramoto--Sivashinsky (gKS) equations. We consider several POD and POD-DEIM projection ROMs with various strategies for parameter sampling and snapshot collection. The aim is to identify an approach for constructing a ROM that is efficient across a range of parameters, encompassing several regimes exhibited by the KS and gKS...
Structure-Aware Tensorial Model Reduction
Announce Type: replace Abstract: This work investigates a two-stage method for constructing projection-based reduced-order models (ROMs) of parameterized partial differential equations (PDEs). Based on established tensorial ROM methodology, the proposed approach reduces dimensionality offline by encoding solution snapshots using a multi-linear Tucker factorization, so that a reduced basis which varies nonlinearly with PDE parameters can be rapidly constructed online and used in a Galerkin...
Surrogate normal-forms for the numerical bifurcation and stability analysis of navier-stokes flows via machine learning
Announce Type: replace-cross Abstract: Inspired by the Equation-Free paradigm, we propose an ``embed-learn-lift'' framework for constructing minimal-dimensional surrogate ROMs for the numerical analysis of high-fidelity Navier-Stokes simulations, even in the presence of symmetries that standard machine-learning surrogates often fail to preserve. The framework consists of four main stages.
Surrogate normal-forms for the numerical bifurcation and stability analysis of navier-stokes flows via machine learning
Announce Type: replace Abstract: Inspired by the Equation-Free paradigm, we propose an ``embed-learn-lift'' framework for constructing minimal-dimensional surrogate ROMs for the numerical analysis of high-fidelity Navier-Stokes simulations, even in the presence of symmetries that standard machine-learning surrogates often fail to preserve. The framework consists of four main stages.
Reduced-order modeling of Hamiltonian dynamics based on symplectic neural networks
arXiv:2508.11911v2 Announce Type: replace Abstract: We introduce a novel data-driven symplectic induced-order modeling (ROM) framework for high-dimensional Hamiltonian systems that unifies latent-space discovery and dynamics learning within a single, end-to-end neural architecture. The encoder-decoder is built from Henon neural networks (HenonNets) and may be augmented with linear SGS-reflector layers. This yields an exact symplectic map between full and latent phase spaces.
Learning Control-Affine Reduced-Order Models via Autoencoders
Announce Type: cross Abstract: We present in this paper a framework for the identification of control-affine reduced-order models (ROMs). The proposed method utilizes autoencoders (AEs) to transform the high-dimensional states, and potentially the high-dimensional inputs, into reduced latent ones suitable for control-affine state-space dynamics. This is achieved by simultaneous training of the AE and the state-space model.
Reduced-order modeling of Hamiltonian dynamics based on symplectic neural networks
arXiv:2508.11911v2 Announce Type: replace-cross Abstract: We introduce a novel data-driven symplectic induced-order modeling (ROM) framework for high-dimensional Hamiltonian systems that unifies latent-space discovery and dynamics learning within a single, end-to-end neural architecture. The encoder-decoder is built from Henon neural networks (HenonNets) and may be augmented with linear SGS-reflector layers. This yields an exact symplectic map between full and latent phase spaces.