186,980 research outputs found
Learning Integrable Dynamics with Action-Angle Networks
Machine learning has become increasingly popular for efficiently modelling
the dynamics of complex physical systems, demonstrating a capability to learn
effective models for dynamics which ignore redundant degrees of freedom.
Learned simulators typically predict the evolution of the system in a
step-by-step manner with numerical integration techniques. However, such models
often suffer from instability over long roll-outs due to the accumulation of
both estimation and integration error at each prediction step. Here, we propose
an alternative construction for learned physical simulators that are inspired
by the concept of action-angle coordinates from classical mechanics for
describing integrable systems. We propose Action-Angle Networks, which learn a
nonlinear transformation from input coordinates to the action-angle space,
where evolution of the system is linear. Unlike traditional learned simulators,
Action-Angle Networks do not employ any higher-order numerical integration
methods, making them extremely efficient at modelling the dynamics of
integrable physical systems.Comment: Accepted at Machine Learning and the Physical Sciences workshop at
NeurIPS 202
Evolution of Privacy Loss in Wikipedia
The cumulative effect of collective online participation has an important and
adverse impact on individual privacy. As an online system evolves over time,
new digital traces of individual behavior may uncover previously hidden
statistical links between an individual's past actions and her private traits.
To quantify this effect, we analyze the evolution of individual privacy loss by
studying the edit history of Wikipedia over 13 years, including more than
117,523 different users performing 188,805,088 edits. We trace each Wikipedia's
contributor using apparently harmless features, such as the number of edits
performed on predefined broad categories in a given time period (e.g.
Mathematics, Culture or Nature). We show that even at this unspecific level of
behavior description, it is possible to use off-the-shelf machine learning
algorithms to uncover usually undisclosed personal traits, such as gender,
religion or education. We provide empirical evidence that the prediction
accuracy for almost all private traits consistently improves over time.
Surprisingly, the prediction performance for users who stopped editing after a
given time still improves. The activities performed by new users seem to have
contributed more to this effect than additional activities from existing (but
still active) users. Insights from this work should help users, system
designers, and policy makers understand and make long-term design choices in
online content creation systems
Sensitivity analysis of sensors in a hydraulic condition monitoring system using CNN models
Condition monitoring (CM) is a useful application in industry 4.0, where the machine’s health is controlled by computational intelligence methods. Data-driven models, especially from the field of deep learning, are efficient solutions for the analysis of time series sensor data due to their ability to recognize patterns in high dimensional data and to track the temporal evolution of the signal. Despite the excellent performance of deep learning models in many applications, additional requirements regarding the interpretability of machine learning models are getting relevant. In this work, we present a study on the sensitivity of sensors in a deep learning based CM system providing high-level information about the relevance of the sensors. Several convolutional neural networks (CNN) have been constructed from a multisensory dataset for the prediction of different degradation states in a hydraulic system. An attribution analysis of the input features provided insights about the contribution of each sensor in the prediction of the classifier. Relevant sensors were identified, and CNN models built on the selected sensors resulted equal in prediction quality to the original models. The information about the relevance of sensors is useful for the system’s design to decide timely on the required sensorsPeer ReviewedPostprint (published version
Physics-Informed Echo State Networks for Chaotic Systems Forecasting
We propose a physics-informed Echo State Network (ESN)
to predict the evolution of chaotic systems. Compared to conventional
ESNs, the physics-informed ESNs are trained to solve supervised learning
tasks while ensuring that their predictions do not violate physical laws.
This is achieved by introducing an additional loss function during the
training of the ESNs, which penalizes non-physical predictions without
the need of any additional training data. This approach is demonstrated
on a chaotic Lorenz system, where the physics-informed ESNs improve
the predictability horizon by about two Lyapunov times as compared to
conventional ESNs. The proposed framework shows the potential of using
machine learning combined with prior physical knowledge to improve the
time-accurate prediction of chaotic dynamical systems
Physics-Informed Echo State Networks for Chaotic Systems Forecasting
We propose a physics-informed Echo State Network (ESN) to predict the
evolution of chaotic systems. Compared to conventional ESNs, the
physics-informed ESNs are trained to solve supervised learning tasks while
ensuring that their predictions do not violate physical laws. This is achieved
by introducing an additional loss function during the training of the ESNs,
which penalizes non-physical predictions without the need of any additional
training data. This approach is demonstrated on a chaotic Lorenz system, where
the physics-informed ESNs improve the predictability horizon by about two
Lyapunov times as compared to conventional ESNs. The proposed framework shows
the potential of using machine learning combined with prior physical knowledge
to improve the time-accurate prediction of chaotic dynamical systems.Comment: 7 pages, 3 figure
Improving Scientific Machine Learning via Attention and Multiple Shooting
Scientific Machine Learning (SciML) is a burgeoning field that
synergistically combines domain-aware and interpretable models with agnostic
machine learning techniques. In this work, we introduce GOKU-UI, an evolution
of the SciML generative model GOKU-nets. GOKU-UI not only broadens the original
model's spectrum to incorporate other classes of differential equations, such
as Stochastic Differential Equations (SDEs), but also integrates attention
mechanisms and a novel multiple shooting training strategy in the latent space.
These enhancements have led to a significant increase in its performance in
both reconstruction and forecast tasks, as demonstrated by our evaluation of
simulated and empirical data. Specifically, GOKU-UI outperformed all baseline
models on synthetic datasets even with a training set 16-fold smaller,
underscoring its remarkable data efficiency. Furthermore, when applied to
empirical human brain data, while incorporating stochastic Stuart-Landau
oscillators into its dynamical core, it not only surpassed all baseline methods
in the reconstruction task, but also demonstrated better prediction of future
brain activity up to 15 seconds ahead. By training GOKU-UI on resting state
fMRI data, we encoded whole-brain dynamics into a latent representation,
learning an effective low-dimensional dynamical system model that could offer
insights into brain functionality and open avenues for practical applications
such as the classification of mental states or psychiatric conditions.
Ultimately, our research provides further impetus for the field of Scientific
Machine Learning, showcasing the potential for advancements when established
scientific insights are interwoven with modern machine learning
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