Physics-Inspired Structural Representations for Molecules and Materials

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Herein, machine learning (ML) models using multiple linear regression (MLR), support vector regression (SVR), random forest (RF) and artificial neural network (ANN) are developed and compared to predict the output features viz. specific capacitance (Csp), ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

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 ...

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 ...

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 ...

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 ...

Human babies have a natural desire to interact with new toys and objects, through which they learn how the world around them works, e.g., that glass shatters when dropped, but a rubber ball does not. When their predictions are proven incorrect, such as whe ...

Examining the performance of an existing bridge requires information on several aspects, such as design choices, material properties, and ongoing degradation processes. It often happens that some critical information is not available. The traditional appro ...

Decision-making permeates every aspect of human and societal development, from individuals' daily choices to the complex decisions made by communities and institutions. Central to effective decision-making is the discipline of optimization, which seeks th ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

In this thesis, we conduct a comprehensive investigation into structural instabilities of both elastic and magneto-elastic beams and shells, resulting in a creative proposal to design a programmable braille reader. Methodologically, we combine numerical si ...

Surface-based inflatables are composed of two thin layers of nearly inextensible sheet material joined together along carefully selected fusing curves. During inflation, pressure forces separate the two sheets to maximize the enclosed volume. The fusing c ...

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 ...

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

Data-driven approaches have been applied to reduce the cost of accurate computational studies on materials, by using only a small number of expensive reference electronic structure calculations for a representative subset of the materials space, and using ...

Bending-active elastica beam is a structural configuration that is based on the elastic deformation of an initially straight beam. This deformation occurs when horizontal displacements are applied to a sliding support, causing the beam to bend into an arch ...

We propose a local, non -intrusive model order reduction technique to accurately approximate the solution of coupled multi -component parametrized systems governed by partial differential equations. Our approach is based on the approximation of the boundar ...

Statistical (machine-learning, ML) models are more and more often used in computational chemistry as a substitute to more expensive ab initio and parametrizable methods. While the ML algorithms are capable of learning physical laws implicitly from data, ad ...

The development of Ultra-High Performance Fibre-Reinforced Cementitious Composite (UHPFRC) has enabled the design of lightweight constructions. Although the greenhouse gas emissions of UHPFRC per volume are higher than that of conventional concr ...

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 ...

Physics-Inspired Structural Representations for Molecules and Materials

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Machine learning models for prediction of electrochemical properties in supercapacitor electrodes using MXene and graphene nanoplatelets

Random matrix methods for high-dimensional machine learning models

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

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

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

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

A global framework for data-informed bridge examination

Reliable data-driven decision-making through optimal transport

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Instabilities in Elastic and Magneto-Elastic Beams and Shells: from Shell Buckling to the Design of a Programmable Braille Reader

Computational Homogenization for Inverse Design of Surface-based Inflatables

Robust machine learning for neuroscientific inference

Graph generative deep learning models with an application to circuit topologies

Efficient and insightful descriptors for representing molecular and material space

Short-term behavior of glass fiber-polymer composite bending-active elastica beam under service load application

Model reduction of coupled systems based on non-intrusive approximations of the boundary response maps

Encoding quantum-chemical knowledge into machine-learning models of complex molecular properties

Methods to assess the carbon footprint of structures involving UHPFRC elements

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