A shortcut model for batch distillation columns using pertinent effective factors

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Integer linear programming (ILP) is an elegant approach to solve linear optimization problems, naturally described using integer decision variables. Within the context of physics-inspired machine learning (ML) applied to chemistry, we demonstrate the relev ...

Synthesizability in generative molecular design remains a pressing challenge. Existing methods to assess synthesizability include heuristics-based metrics or retrosynthesis models which predict a synthetic pathway. By contrast, an explicit approach anchors ...

This work aims to develop a scalable surrogate modelling framework for robust multidisciplinary design optimisation (RMDO) in complex engineering systems. Focusing on gas-bearing supported rotors, it addresses the need to maintain system performance under ...

We formulate computed-tomography reconstruction as a continuous-domain optimization problem with Hessian total variation (HTV) as the regularizer. HTV is a sparsity-promoting regularizer that favors continuous and piecewise-linear functions with few affine ...

The near-azeotrope mixture of TFE and CO2 is an important concern urging the scientific community to develop new ways for TFE/CO2 separations. In this work, for the first time, Diisopropylamine (DIPA) and Diethanolamine (DEA) are used as solvents for separ ...

Wind energy is one of the potential options to fill the gap in renewable energy production in Switzerland during the winter season when the energy demand exceeds local production capacities. With likely further rising energy consumption in the future, the ...

This paper presents a new Matlab toolbox, aimed at facilitating the use of polynomial optimization for stability analysis of nonlinear systems. In the past decade several decisive contributions made it possible to recast this type of problems as convex opt ...

This paper presents a distributed shadow dynamic consensus (DSDC) framework to address the generation company (GENCO) strategic bidding problem in the independent system operator (ISO) operation. First, each sub-group is defined according to the communicat ...

Kalman and H-infinity filters, the most popular paradigms for linear state estimation, are designed for very specific specific noise and disturbance patterns, which may not appear in practice. State observers based on the minimization of regret measures ar ...

This paper studies the problem of online performance optimization of constrained closed-loop control systems, where both the objective and the constraints are unknown black-box functions affected by exogenous time-varying contextual disturbances. A primal- ...

We develop a principled approach to end-to-end learning in stochastic optimization. First, we show that the standard end-to-end learning algorithm admits a Bayesian interpretation and trains a posterior Bayes action map. Building on the insights of this an ...

In this article, we consider decentralized optimization problems where agents have individual cost functions to minimize subject to subspace constraints that require the minimizers across the network to lie in low-dimensional subspaces. This constrained fo ...

How the 'what', 'where', and 'when' of past experiences are stored in episodic memories and retrieved for suitable decisions remains unclear. In an effort to address these questions, the authors present computational models of neural networks that behave l ...

Microbubbles excited by acoustic fields inside water oscillate, and generate acoustic radiation forces and drag-induced acoustic streaming. These forces can be harnessed in various biomedical applications such as targeted drug delivery and on-chip biomanip ...

One of the main goal of Artificial Intelligence is to develop models capable of providing valuable predictions in real-world environments. In particular, Machine Learning (ML) seeks to design such models by learning from examples coming from this same envi ...

Herein we report a multi-zone, heating, ventilation and air-conditioning (HVAC) control case study of an industrial plant responsible for cooling a hospital surgery center. The adopted approach to guaranteeing thermal comfort and reducing electrical energy ...

In superstructure optimization of processes and energy systems, the design space is defined as the combination of unit considerations, process conditions and model parameters that might be subjected to uncertainty. Most of the time, decision makers are not ...

An important requirement of wastewater treatment plants (WWTPs) is compliance with the local regulations on effluent discharge, which are going to become more stringent in the future. The operation of WWTPs exhibits a trade-off between operational cost and ...

Graph Signal Processing (GSP) is an emerging research field that extends the concepts of digital signal processing to graphs. GSP has numerous applications in different areas such as sensor networks, machine learning, and image processing. The sampling and ...

This paper reviews the state of the art and discusses recent developments in the field of adaptive isogeometric analysis, with special focus on the mathematical theory. This includes an overview of available spline technologies for the local resolution of ...

A shortcut model for batch distillation columns using pertinent effective factors

Chattez avec Graph Search

Integer Linear Programming for Unsupervised Training Set Selection in Molecular Machine Learning

Directly optimizing for synthesizability in generative molecular design using retrosynthesis models

Dimensionless Group-Driven Ensemble Neural Networks for Robust Design Optimisation in Engineering

A Continuous-Domain Solution for Computed Tomography with Hessian Total-Variation Regularization

Separation of CO2 and TFE by using diethanolamine and diisopropylamine

Influence of air flow features on alpine wind energy potential

SOStab: A Matlab toolbox for transient stability analysis

Distributed Shadow Dynamic Consensus Optimization Framework for Power Market with Parameterization Bidding Strategy of GENCOs

Minimal regret state estimation of time-varying systems

Primal-Dual Contextual Bayesian Optimization for Control System Online Optimization with Time-Average Constraints

End-to-End Learning for Stochastic Optimization: A Bayesian Perspective

Quantization for Decentralized Learning Under Subspace Constraints

Computational models of episodic-like memory in food-caching birds

Dynamics of entrapped microbubbles with multiple openings

Predicting in Uncertain Environments: Methods for Robust Machine Learning

Experimental data-driven model predictive control of a hospital HVAC system during regular use

Active learning for multi-objective optimization of processes and energy systems

Real-Time Optimization of Wastewater Treatment Plants via Constraint Adaptation

Reconstruction of Time-Varying Graph Signals via Sobolev Smoothness

Mathematical Foundations of Adaptive Isogeometric Analysis