Gaussian Process Regression for Materials and Molecules

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The interaction between molecules and plasmonic nanoparticles significantly influences molecular emission properties, offering opportunities to enhance fluorescence through the Purcell effect and strong coupling phenomena. While previous studies have focus ...

The recent introduction of high entropy alloys presents an exciting opportunity for novel material performance, offering extensive possibilities for tailoring material properties. Dislocation mechanisms in these alloys must be fully understood since they p ...

We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of Double-struck capital R. An order-1 autoregressive model in this context is to be understood as a Markov chain, where ...

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 ...

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 ...

Consider the wave equation with heterogeneous coefficients in the homogenization regime. At large times, the wave interacts in a nontrivial way with the heterogeneities, giving rise to effective dispersive effects. The main achievement of the present wor ...

The emergence of mycelium-bonded biocomposites has been stimulated in the quest for sustainable development. These biocomposites exhibit enhanced mechanical properties through hot pressing, with mycelium playing a crucial role as both the adhesive and rein ...

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 ...

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 ...

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 ...

Deep learning has brought incredible progress in areas where traditional algorithms struggle. It can be because they need rules that are sometimes hard to conceptualize, it can also be that the number and manner of combining these rules are too complex to ...

Two design studios were successively taught in two schools of architecture: one at the Harvard Graduate School of Design (GSD) and the other at the Swiss Federal Institute of Technology in Lausanne (EPFL) in Lausanne, Switzerland, based on "A Global Morato ...

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 ...

This manuscript is the lecture notes of B. Barak’s course in the Les Houches ‘Statistical Physics and Machine Learning’ summer school in 2022. It surveys various proxies for computational hardness in random planted problems, from the low-degree likelihood ...

In this paper, we present a new method for upcycling concrete rubble waste into slender walls through the lightweight digital augmentation of mainstream construction machines. By using such method, the environmental impact of concrete construction and demo ...

The design point that had been chosen for EU DEMO in 2016 is reviewed here and a modification is proposed with a lower aspect ratio. Previously the same aspect ratio, A, was chosen for EU DEMO as in major tokamak experiments including ITER (A = 3.1), and, ...

Carefully extracting reinforced concrete (RC) elements from soon-to-be demolished structures and reusing them as load-bearing components is an emerging circular low-carbon alternative to building new structures. As floor construction typically accounts for ...

Digital technology has become an integral part of our lives, offering various benefits from communication and entertainment to education and productivity. However, its pervasive use has also raised concerns about its potential negative impact on users' wel ...

Recent advancements in deep learning have found compelling applications in the image-based inspection of infrastructure systems. However, the efficacy of these models hinges significantly on the quality and diversity of the input data used for training. In ...

Determining whether a molecule can be synthesized is crucial in chemistry and drug discovery, as it guides experimental prioritization and molecule ranking in de novo design tasks. Existing scoring approaches to assess synthetic feasibility struggle to ext ...

Gaussian Process Regression for Materials and Molecules

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Tuning Molecular Emission: Exploring Plasmonic Coupling Effects across High and Low Quantum Yields

Effects of Rate-dependent Plasticity on Residual Stresses in Additive Manufacturing of Complex Alloys

On distributional autoregression and iterated transportation

Seebeck Coefficient of Ionic Conductors from Bayesian Regression Analysis

Efficient and insightful descriptors for representing molecular and material space

A Spectral Ansatz for The Long-Time Homogenization of The Wave Equation

Investigation of mycelium film as the adhesive for poplar veneer bonding: insight into interfacial bonding mechanisms

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

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

Computational Homogenization for Inverse Design of Surface-based Inflatables

Extending Capabilities of Attention-based Models

Teaching Not To

Random matrix methods for high-dimensional machine learning models

Computational complexity of deep learning: fundamental limitations and empirical phenomena

Rising from rubble - Leveraging existing construction tools for upcycling concrete waste into slender walls

Re-design of EU DEMO with a low aspect ratio

FLO:RE – A new floor system made of reused reinforced concrete and steel elements

Understanding and Enhancing Digital Wellbeing Dimensions: Empirical Studies on Smartphone Usage Patterns and Recommendations of Micro-Informative Content

Uncertainty quantification for a deep learning models for image-based crack segmentation

FSscore: A Personalized Machine Learning-Based Synthetic Feasibility Score