Computational Design of Weingarten Surfaces

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In this paper we will consider distributed Linear-Quadratic Optimal Control Problems dealing with Advection-Diffusion PDEs for high values of the Peclet number. In this situation, computational instabilities occur, both for steady and unsteady cases. A Str ...

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We develop new tools to study landscapes in nonconvex optimization. Given one optimization problem, we pair it with another by smoothly parametrizing the domain. This is either for practical purposes (e.g., to use smooth optimization algorithms with good g ...

A method for optimizing at least one of a geometry, an implantation procedure, and/or stimulation protocol of one or more electrodes for an electrical stimulation of a target structure in a nervous system of a living being by a computer device, the method ...

Hyperbolic lattices are a new type of synthetic materials based on regular tessellations in non-Euclidean spaces with constant negative curvature. While so far, there has been several theoretical investigations of hyperbolic topological media, experimental ...

Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal calls to oracles (computational property predictors). This problem becomes more apparen ...

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Bayesian Optimization (BO) is typically used to optimize an unknown function f that is noisy and costly to evaluate, by exploiting an acquisition function that must be maximized at each optimization step. Even if provably asymptotically optimal BO algorith ...

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Discrete domes are doubly curved structures comprising a network of beam-like elements. We study the mechanics of discrete domes made of ribbons woven in a pentagonal triaxial pattern. Experiments and finite element simulations are performed to characteriz ...

This work presents a new computational optimization framework for the robust control of parks of Wave Energy Converters (WEC) in irregular waves. The power of WEC parks is maximized with respect to the individual control damping and stiffness coefficients ...

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In Process Systems Engineering, computationally-demanding models are frequent and plentiful. Handling such complexity in an optimization framework in a fast and reliable way is essential, not only for generating meaningful solutions but also for providing ...

Simplicial Kuramoto models have emerged as a diverse and intriguing class of models describing oscillators on simplices rather than nodes. In this paper, we present a unified framework to describe different variants of these models, categorized into three ...

We introduce a computational pipeline for simulating and designing C-shells, a new class of planar-to-spatial deployable linkage structures. A C-shell is composed of curved flexible beams connected at rotational joints that can be assembled in a stress-fre ...

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Organocatalysis has evolved significantly over the last decades, becoming a pillar of synthetic chemistry, but traditional theoretical approaches based on quantum mechanical computations to investigate reaction mechanisms and provide rationalizations of ca ...

Computational Design of Weingarten Surfaces

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A Streamline Upwind Petrov-Galerkin Reduced Order Method for Advection-Dominated Partial Differential Equations Under Optimal Control

Scalable constrained optimization

The effect of smooth parametrizations on nonconvex optimization landscapes

A method and system for optimizing design, implantation, and stimulation protocols of electrodes stimulating the nervous system

Anomalous and Chern topological waves in hyperbolic networks

Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

A ride time-oriented scheduling algorithm for dial-a-ride problems

Relaxing the Additivity Constraints in Decentralized No-Regret High-Dimensional Bayesian Optimization

Automated all-functionals infrared and Raman spectra

Robot Learning using Tensor Networks

Indentation and stability of woven domes

Robust optimization of control parameters for WEC arrays using stochastic methods

Stochastic adaptive resampling for the estimation of discrete choice models

Bayesian Optimization for Chemical Reactions

In-situ atomic level observation of the strain response of graphene lattice

A combined genetic algorithm and active learning approach to build and test surrogate models in Process Systems Engineering

A unified framework for Simplicial Kuramoto models

C-shells: Deployable Gridshells with Curved Beams

Piecewise Affine Curvature model: a reduced-order model for soft robot-environment interaction beyond PCC

Enriching the Computational Toolbox for Organocatalysis