90 research outputs found
Stability of implicit neural networks for long-term forecasting in dynamical systems
Forecasting physical signals in long time range is among the most challenging
tasks in Partial Differential Equations (PDEs) research. To circumvent
limitations of traditional solvers, many different Deep Learning methods have
been proposed. They are all based on auto-regressive methods and exhibit
stability issues. Drawing inspiration from the stability property of implicit
numerical schemes, we introduce a stable auto-regressive implicit neural
network. We develop a theory based on the stability definition of schemes to
ensure the stability in forecasting of this network. It leads us to introduce
hard constraints on its weights and propagate the dynamics in the latent space.
Our experimental results validate our stability property, and show improved
results at long-term forecasting for two transports PDEs.Comment: ICLR 2023 Workshop on Physics for Machine Learnin
Functional maps representation on product manifolds
We consider the tasks of representing, analysing and manipulating maps between shapes. We model maps as densities over the product manifold of the input shapes; these densities can be treated as scalar functions and therefore are manipulable using the language of signal processing on manifolds. Being a manifold itself, the product space endows the set of maps with a geometry of its own, which we exploit to define map operations in the spectral domain; we also derive relationships with other existing representations (soft maps and functional maps). To apply these ideas in practice, we discretize product manifolds and their Laplace–Beltrami operators, and we introduce localized spectral analysis of the product manifold as a novel tool for map processing. Our framework applies to maps defined between and across 2D and 3D shapes without requiring special adjustment, and it can be implemented efficiently with simple operations on sparse matrices
DS-GPS : A Deep Statistical Graph Poisson Solver (for faster CFD simulations)
This paper proposes a novel Machine Learning-based approach to solve a
Poisson problem with mixed boundary conditions. Leveraging Graph Neural
Networks, we develop a model able to process unstructured grids with the
advantage of enforcing boundary conditions by design. By directly minimizing
the residual of the Poisson equation, the model attempts to learn the physics
of the problem without the need for exact solutions, in contrast to most
previous data-driven processes where the distance with the available solutions
is minimized
Reconstruction of turbulent data with deep generative models for semantic inpainting from TURB-Rot database
We study the applicability of tools developed by the computer vision
community for features learning and semantic image inpainting to perform data
reconstruction of fluid turbulence configurations. The aim is twofold. First,
we explore on a quantitative basis, the capability of Convolutional Neural
Networks embedded in a Deep Generative Adversarial Model (Deep-GAN) to generate
missing data in turbulence, a paradigmatic high dimensional chaotic system. In
particular, we investigate their use in reconstructing two-dimensional damaged
snapshots extracted from a large database of numerical configurations of 3d
turbulence in the presence of rotation, a case with multi-scale random features
where both large-scale organised structures and small-scale highly intermittent
and non-Gaussian fluctuations are present. Second, following a reverse
engineering approach, we aim to rank the input flow properties (features) in
terms of their qualitative and quantitative importance to obtain a better set
of reconstructed fields. We present two approaches both based on Context
Encoders. The first one infers the missing data via a minimization of the L2
pixel-wise reconstruction loss, plus a small adversarial penalisation. The
second searches for the closest encoding of the corrupted flow configuration
from a previously trained generator. Finally, we present a comparison with a
different data assimilation tool, based on Nudging, an equation-informed
unbiased protocol, well known in the numerical weather prediction community.
The TURB-Rot database, http://smart-turb.roma2.infn.it, of roughly 300K 2d
turbulent images is released and details on how to download it are given
Color Recommendation for Vector Graphic Documents based on Multi-Palette Representation
Vector graphic documents present multiple visual elements, such as images,
shapes, and texts. Choosing appropriate colors for multiple visual elements is
a difficult but crucial task for both amateurs and professional designers.
Instead of creating a single color palette for all elements, we extract
multiple color palettes from each visual element in a graphic document, and
then combine them into a color sequence. We propose a masked color model for
color sequence completion and recommend the specified colors based on color
context in multi-palette with high probability. We train the model and build a
color recommendation system on a large-scale dataset of vector graphic
documents. The proposed color recommendation method outperformed other
state-of-the-art methods by both quantitative and qualitative evaluations on
color prediction and our color recommendation system received positive feedback
from professional designers in an interview study.Comment: Accepted to WACV 202
From Data to Knowledge Graphs: A Multi-Layered Method to Model User's Visual Analytics Workflow for Analytical Purposes
The importance of knowledge generation drives much of Visual Analytics (VA).
User-tracking and behavior graphs have shown the value of understanding users'
knowledge generation while performing VA workflows. Works in theoretical
models, ontologies, and provenance analysis have greatly described means to
structure and understand the connection between knowledge generation and VA
workflows. Yet, two concepts are typically intermixed: the temporal aspect,
which indicates sequences of events, and the atemporal aspect, which indicates
the workflow state space. In works where these concepts are separated, they do
not discuss how to analyze the recorded user's knowledge gathering process when
compared to the VA workflow itself. This paper presents Visual Analytic
Knowledge Graph (VAKG), a conceptual framework that generalizes existing
knowledge models and ontologies by focusing on how humans relate to computer
processes temporally and how it relates to the workflow's state space. Our
proposal structures this relationship as a 4-way temporal knowledge graph with
specific emphasis on modeling the human and computer aspect of VA as separate
but interconnected graphs for, among others, analytical purposes. We compare
VAKG with relevant literature to show that VAKG's contribution allows VA
applications to use it as a provenance model and a state space graph, allowing
for analytics of domain-specific processes, usage patterns, and users'
knowledge gain performance. We also interviewed two domain experts to check, in
the wild, whether real practice and our contributions are aligned.Comment: 9 pgs, submitted to VIS 202
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