Publications related to Getting more out of an incommensurately modulated structure: the example of K_5Yb(MoO_4)_4

High-order geometric integrators for the variational Gaussian wavepacket dynamics and application to vibronic spectra at finite temperature

Molecular quantum dynamics simulations are essential for understanding many fundamental phenomena in physics and chemistry. They often require solving the time-dependent Schrödinger equation for molecular nuclei, which is challenging even for medium-sized ...

EPFL2024

A Reduced Order Model for Domain Decompositions with Non-conforming Interfaces

Alfio Quarteroni, Andrea Manzoni

In this paper, we propose a reduced-order modeling strategy for two-way Dirichlet-Neumann parametric coupled problems solved with domain-decomposition (DD) sub-structuring methods. We split the original coupled differential problem into two sub-problems wi ...

New York2024

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Paul Scharnhorst

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

EPFL2024

Efficient Continual Finite-Sum Minimization

Volkan Cevher, Efstratios Panteleimon Skoulakis, Leello Tadesse Dadi

Given a sequence of functions

f_1,\ldots,f_n

with

f_i:\mathcal{D}\mapsto \mathbb{R}

, finite-sum minimization seeks a point

{x}^\star \in \mathcal{D}

minimizing

\sum_{j=1}^nf_j(x)/n

. In this work, we propose a key twist into the finite-sum minimizat ...

2024

Residual-based attention in physics-informed neural networks

Nikolaos Stergiopoulos, Sokratis Anagnostopoulos

Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

Lausanne2024

SIMPLE HIERARCHICAL PLANNING WITH DIFFUSION

Caglar Gulcehre, Chang Chen

Diffusion-based generative methods have proven effective in modeling trajectories with offline datasets. However, they often face computational challenges and can falter in generalization, especially in capturing temporal abstractions for long- horizon tas ...

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Micropreparative Gel Electrophoresis for Purification of Nanoscale Bioconjugates

Ardemis Anoush Boghossian, Yahya Rabbani, Shang-Jung Wu, Vitalijs Zubkovs, Sayyed Hashem Sajjadi

Conventional techniques for purifying macromolecular conjugates often require complex and costly installments that are inaccessible to most laboratories. In this work, we develop a one-step micropreparative method based on a trilayered polyacrylamide gel e ...

2024

Modeling friction and wear using an adaptive discrete-continuum coupling

Manon Eugénie Voisin--Leprince

When two objects slide against each other, wear and friction occur at their interface. The accumulation of wear forms what is commonly referred to as a ``third-body''. Understanding third-body evolution has significant applications in industry, where contr ...

EPFL2024

Iterative removal of sources to model the turbulent electromotive force

Abhijit Bhausaheb Bendre

We describe a novel method to compute the components of dynamo tensors from direct magnetohydrodynamic (MHD) simulations. Our method relies upon an extension and generalization of the standard H & ouml;gbom CLEAN algorithm widely used in radio astronomy to ...

Oxford Univ Press2024

Energy-Efficient Frequency Selection Method for Bio-Signal Acquisition in AI/ML Wearables

David Atienza Alonso, Miguel Peon Quiros, José Angel Miranda Calero, Hossein Taji

In wearable sensors, energy efficiency is crucial, particularly during phases where devices are not processing, but rather acquiring biosignals for subsequent analysis. This study focuses on improving the power consumption of wearables during these acquisi ...

2024

Getting more out of an incommensurately modulated structure: the example of K_5Yb(MoO_4)_4

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High-order geometric integrators for the variational Gaussian wavepacket dynamics and application to vibronic spectra at finite temperature

A Reduced Order Model for Domain Decompositions with Non-conforming Interfaces

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Efficient Continual Finite-Sum Minimization

Residual-based attention in physics-informed neural networks

SIMPLE HIERARCHICAL PLANNING WITH DIFFUSION

Micropreparative Gel Electrophoresis for Purification of Nanoscale Bioconjugates

Modeling friction and wear using an adaptive discrete-continuum coupling

Iterative removal of sources to model the turbulent electromotive force

Energy-Efficient Frequency Selection Method for Bio-Signal Acquisition in AI/ML Wearables

High-order geometric integrators for the variational Gaussian wavepacket dynamics and application to vibronic spectra at finite temperature

A Reduced Order Model for Domain Decompositions with Non-conforming Interfaces

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Efficient Continual Finite-Sum Minimization

Residual-based attention in physics-informed neural networks

SIMPLE HIERARCHICAL PLANNING WITH DIFFUSION

Micropreparative Gel Electrophoresis for Purification of Nanoscale Bioconjugates

Modeling friction and wear using an adaptive discrete-continuum coupling

Iterative removal of sources to model the turbulent electromotive force

Energy-Efficient Frequency Selection Method for Bio-Signal Acquisition in AI/ML Wearables