Structure-preserving approaches and data-driven closure modeling for model order reduction

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The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Neurons in the human amygdala and hippocampus are classically thought to encode a person’s identity invariant to visual features. However, it remains largely unknown how visual information from higher visual cortical areas is translated into such a semanti ...

We present a finite elements-neural network approach for the numerical approximation of parametric partial differential equations. The algorithm generates training data from finite element simulations, and uses a data -driven (supervised) feedforward neura ...

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

Facial paralysis is a highly burdening condition, resulting in a patient's inability to move his musculature on one or both sides of the mouth. The restoration of facial movement is crucial for enhancing the overall patients' quality of life. The use of DE ...

“You never forget how to ride a bike”, – but how is that possible? The brain is able to learn complex skills, stop the practice for years, learn other skills in between, and still retrieve the original knowledge when necessary. The mechanisms of this capab ...

The thesis is dedicated to the study of two main partial differential equations (PDEs) in fluid dynamics: the Navier-Stokes equations, which describe the motion of incompressible fluids, and the transport equation with divergence-free velocity fields, whic ...

In a previous paper, a new gyrokinetic quasilinear model was proposed to study turbulent momentum transport in tokamaks with up-down asymmetric plasma shaping and toroidal flow shear (Sun et al 2024 Nuclear Fusion 64 036026). In this work, we start by appl ...

Rigid body interactions are fundamental to numerous scientific disciplines, but remain challenging to simulate due to their abrupt nonlinear nature and sensitivity to complex, often unknown environmental factors. These challenges call for adaptable learnin ...

Throughout history, the pace of knowledge and information sharing has evolved into an unthinkable speed and media. At the end of the XVII century, in Europe, the ideas that would shape the "Age of Enlightenment" were slowly being developed in coffeehouses, ...

Contemporary AI faces important limitations that remain unresolved despite significant successes. Chief among these are the inability to acquire new knowledge without destroying old, the incomprehensible and nonengineerable nature of internal representatio ...

Predicting the evolution of systems with spatio-temporal dynamics in response to external stimuli is essential for scientific progress. Traditional equations-based approaches leverage first principles through the numerical approximation of differential equ ...

Real-time bioaerosol monitoring is improving the quality of life for people affected by allergies, but it often relies on deep learning models which pose challenges for widespread adoption. These models are typically trained in a supervised fashion and req ...

In this work, we analyze space-time reduced basis methods for the efficient numerical simulation of haemodynamics in arteries. The classical formulation of the reduced basis (RB) method features dimensionality reduction in space, while finite difference sc ...

To address the global loss of biodiversity in freshwater environments, understanding river habitat requirements for key species, such as brown trout (Salmo trutta L.), is crucial. The Habitat Suitability Index (HSI) aids ecologists in identifying preferred ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

In less than a century, electronic computers have become essential to everyday life and have transformed scientific research. Their use in solving scientific problems has integrated theoretical and experimental approaches, often blurring the lines between ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Differences in acquisition protocols or hardware result in measurable changes in image characteristics. These differences affect distributional properties and can also affect the spatial and temporal characteristics of the images. Likewise, the distributio ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Structure-preserving approaches and data-driven closure modeling for model order reduction

Chattez avec Graph Search

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Feature-based encoding of face identity by single neurons in the human amygdala and hippocampus

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

DEMO: Dielectric elastomer actuators (DEAs) for the restoration of facial movements

Context selectivity with dynamic availability enables lifelong continual learning

Anomalous dissipation and other non-smooth phenomena in fluids

A Quasilinear Model for Momentum Transport Including Rotation and Electron-scale Turbulence

Integrating Physics and Topology in Neural Networks for Learning Rigid Body Dynamics

Machine Learning for Modeling Stock Returns

Foundations of a New Learning Paradigm in AI Grounded in the Principles of Evolutionary Developmental Biology

Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks

Self-supervised and few-shot learning for robust bioaerosol monitoring

SPACE-TIME REDUCED BASIS METHODS FOR PARAMETRIZED UNSTEADY STOKES EQUATIONS

Assessing the Weighted Usable Area for the Habitat Suitability of brown trout (Salmo trutta L.) in rivers

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Simulating Interacting Quantum Systems with Near-Term Quantum Computers

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Domain shift, domain adaptation, and generalization: A focus on MRI

Robust machine learning for neuroscientific inference