Robust system identification and model predictions in the presence of systematic uncertainty

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Extracting the relationship between high-dimensional neural recordings and complex behavior is a ubiquitous problem in neuroscience. Encoding and decoding models target the conditional distribution of neural activity given behavior and vice versa, while di ...

This work investigates adversarial training in the context of margin-based linear classifiers in the high-dimensional regime where the dimension d and the number of data points n diverge with a fixed ratio α = n/d. We introduce a tractable mathematical mod ...

As a follow-up to the CEA-EPFL PETALE experimental program on stainless steel nuclear data, the EPFL initiated an open-loop pile-oscillation experimental program in the CROCUS reactor: BLOOM. A reproduction of the critical experiments of PETALE, the progra ...

Gyrotrons are essential devices for the development of nuclear fusion, but their design and reliable operation still present numerous challenges. In particular, certain gyrotrons encounter issues related to trapped electron clouds in the magnetron injectio ...

Operators from various industries have been pushing the adoption of wireless sensing nodes for industrial monitoring, and such efforts have produced sizeable condition monitoring datasets that can be used to build diagnosis algorithms capable of warning ma ...

Image information about the state of a building after an earthquake, which can be collected without endangering the post-earthquake reconnaissance activities, can be used to reduce uncertainties in response predictions for future seismic events. This paper ...

High-level waste, stemming from nuclear electricity generation poses significant environmental and safety concerns. Currently, high-level wastes are stored in interim facilities needing constant monitoring and waiting for a definitive solution. Deep geolog ...

We propose a novel approach to evaluating the ionic Seebeck coefficient in electrolytes from relatively short equilibrium molecular dynamics simulations, based on the Green-Kubo theory of linear response and Bayesian regression analysis. By exploiting the ...

For the dimensioning and assessment of structures, it is common practice to compare action effects with sectional resistances. Extensive studies have been performed to quantify the model uncertainty on the resistance side. However, for statically indetermi ...

Most codes of practice adopt a semi probabilistic design approach for the dimensioning and assessment of structures. Accordingly, structural safety is ensured by performing limit state verifications using design values determined with adequately calibrated ...

We consider fluid flows, governed by the Navier-Stokes equations, subject to a steady symmetry-breaking bifurcation and forced by a weak noise acting on a slow timescale. By generalizing the multiple-scale weakly nonlinear expansion technique employed in t ...

This thesis links two realms of particle accelerator dynamics and precision particle physics. The achievement of precise luminosity measurement at hadron colliders is enabled with dedicated luminometers. For the Run 3 period, the luminometer upgrade was ...

Battery health prediction is significant while challenging for intelligent battery management. This article proposes a general framework for both short-term and long-term predictions of battery health under unseen dynamic loading and temperature conditions ...

The measurement of Z boson production is presented as a method to determine the integrated luminosity of CMS data sets. The analysis uses proton-proton collision data, recorded by the CMS experiment at the CERN LHC in 2017 at a center-of-mass energy of 13 ...

Macroscopic fundamental diagrams (MFDs) have been widely adopted to model the traffic flow of large-scale urban networks. Coupling perimeter control and regional route guidance (PCRG) is a promising strategy to decrease congestion heterogeneity and reduce ...

As large, data-driven artificial intelligence models become ubiquitous, guaranteeing high data quality is imperative for constructing models. Crowdsourcing, community sensing, and data filtering have long been the standard approaches to guaranteeing or imp ...

Dynamic downscaling of atmospheric forcing data to the hectometer resolution has shown increases in accuracy for landsurface models, but at great computational cost. Here we present a validation of a novel intermediate complexity atmospheric model, HICAR, ...

This paper proposes a data-driven control design method for nonlinear systems that builds upon the Koopman operator framework. In particular, the Koopman operator is used to lift the nonlinear dynamics to a higher-dimensional space where the so-called obse ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

Beliefs inform the behaviour of forward-thinking agents in complex environments. Recently, sequential Bayesian inference has emerged as a mechanism to study belief formation among agents adapting to dynamical conditions. However, we lack critical theory to ...

Robust system identification and model predictions in the presence of systematic uncertainty

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Modeling conditional distributions of neural and behavioral data with masked variational autoencoders

A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs

Design of an Open-Loop Pile-Oscillation Program in the CROCUS Reactor

Electron trapping in gyrotron electron guns: Validation of the FENNECS code with the T-REX experiment

Federated learning with uncertainty-based client clustering for fleet-wide fault diagnosis

Reducing uncertainties in response predictions of earthquake-damaged masonry buildings using data from image-based inspection

A FEM modelling workflow to simulate THM effects in the rock around nuclear waste packages

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Model uncertainties in action effects and load bearing capacity calculation in statically indeterminate reinforced concrete structures

Influence of model uncertainty and long term deformations in action effects calculation in reinforced concrete structures

Noise-induced transitions past the onset of a steady symmetry-breaking bifurcation: The case of the sudden expansion

Precision luminosity measurement at hadron colliders

Health Prediction for Lithium-Ion Batteries Under Unseen Working Conditions

Luminosity determination using Z boson production at the CMS experiment

An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance

Extensions of Peer Prediction Incentive Mechanisms

Intermediate complexity atmospheric modeling in complex terrain: is it right?

Data-Driven Control Synthesis using Koopman Operator: A Robust Approach

Spectral Estimators for High-Dimensional Matrix Inference

Stochastic pairwise preference convergence in Bayesian agents