Shape constrained splines as transparent black-box models for bioprocess modeling

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

This paper introduces a novel method for data-driven robust control of nonlinear systems based on the Koopman operator, utilizing Integral Quadratic Constraints (IQCs). The Koopman operator theory facilitates the linear representation of nonlinear system d ...

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

In this paper a new nonlinear identification method for microgrids based on neural networks is proposed. The system identification process can be done using the available closed-loop system input/output data recorded during normal operation without additio ...

This work aims to study the effects of wind uncertainties in civil engineering structural design. Optimising the design of a structure for safety or operability without factoring in these uncertainties can result in a design that is not robust to these per ...

This paper deals with the control-oriented modeling of a multilayered dielectric elastomer actuator based tube. The actuator is clamped at both sides and performs a radial displacement. The hyperelastic deformation and the viscoelastic performance, togethe ...

We introduce a new numerical method for the time-dependent Maxwell equations on unstructured meshes in two space dimensions. This relies on the introduction of a new mesh, which is the barycentric-dual cellular complex of the starting simplicial mesh, and ...

Many engineering fields rely on frequency-domain dynamical systems for the mathematical modeling of physical (electrical/mechanical/etc.) structures. With the growing need for more accurate and reliable results, the computational burden incurred by frequen ...

The work proposes a multi-modal regional mean speed regression analysis for the city network of Athens, Greece. The dataset from pNUEMA experiment is used in the present context. Accumulations and mean speeds of different modes are estimated and compared t ...

A reduced basis method based on a physics-informed machine learning framework is developed for efficient reduced-order modeling of parametrized partial differential equations (PDEs). A feedforward neural network is used to approximate the mapping from the ...

Accurate prediction of travel time is an essential feature to support Intelligent Transportation Systems (ITS). The non-linearity of traffic states, however, makes this prediction a challenging task. Here we propose to use dynamic linear models (DLMs) to a ...

In this study, we examined the removal kinetics of two fluoroquinolones (FQs), flumequine (FLU) and ciprofloxacin (CIP), in synthetic wastewater (SWW) and real hospital wastewater (RHW) using FeOx thin films, peroxymonosulfate (PMS) and visible light. Nano ...

We propose a non-intrusive reduced basis (RB) method for parametrized nonlinear partial differential equations (PDEs) that leverages models of different accuracy. From a collection of low-fidelity (LF) snapshots, parameter locations are extracted for the e ...

Optical soliton molecules are bound states of solitons that arise from the balance between attractive and repulsive effects. Having been observed in systems ranging from optical fibres to mode-locked lasers, they provide insights into the fundamental inter ...

In the presence of plant-model mismatch, the estimation of plant gradients is key to the performance of measurement-based iterative optimization schemes. However, gradient estimation requires time-consuming experiments, wherein the plant is sequentially pe ...

Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of mixed multinomial logit (MMNL) models. It has been established that VB is substan ...

We tackle modelling and inference for variable selection in regression problems with many predictors and many responses. We focus on detecting hotspots, that is, predictors associated with several responses. Such a task is critical in statistical genetics, ...

Information theory plays an indispensable role in the development of algorithm-independent impossibility results, both for communication problems and for seemingly distinct areas such as statistics and machine learning. While numerous information-theoretic ...

Many decision problems in science, engineering and economics are affected by uncertain parameters whose distribution is only indirectly observable through samples. The goal of data-driven decision-making is to learn a decision from finitely many training s ...

Multiple generalized additive models are a class of statistical regression models wherein parameters of probability distributions incorporate information through additive smooth functions of predictors. The functions are represented by basis function expan ...

Chemists continuously harvest the power of non-covalent interactions to control phenomena in both the micro- and macroscopic worlds. From the quantum chemical perspective, the strategies essentially rely upon an in-depth understanding of the physical origi ...

Shape constrained splines as transparent black-box models for bioprocess modeling

Chat with Graph Search

Koopman-based Data-driven Robust Control of Nonlinear Systems Using Integral Quadratic Constraints

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Closed-loop data-driven modeling and distributed control for islanded microgrids with input constraints

Multi-Level Monte Carlo Methods for Uncertainty Quantification and Risk-Averse Optimisation

Control-oriented modeling and analysis of tubular dielectric elastomer actuators dedicated to cardiac assist devices

An arbitrary-order Cell Method with block-diagonal mass-matrices for the time-dependent 2D Maxwell equations

Model order reduction based on functional rational approximants for parametric PDEs with meromorphic structure

Linear regression analysis of regional mean speed of Athens city network using drone data: A multi-modal approach

Physics-informed machine learning for reduced-order modeling of nonlinear problems

Travel Time Prediction for Congested Freeways With a Dynamic Linear Model

Nano-sized iron oxides supported on polyester textile to remove fluoroquinolones in hospital wastewater

A non-intrusive multifidelity method for the reduced order modeling of nonlinear problems

Heteronuclear soliton molecules in optical microresonators

A note on efficient computation of privileged directions in modifier adaptation

Bayesian estimation of mixed multinomial logit models: Advances and simulation-based evaluations

A Global-Local Approach For Detecting Hotspots In Multiple-Response Regression

An Introductory Guide to Fano's Inequality with Applications in Statistical Estimation

Wasserstein Distributionally Robust Optimization: Theory and Applications in Machine Learning

Automatic L2 Regularization for Multiple Generalized Additive Models

Electron density learning of non-covalent systems