Inverse design of metal-organic frameworks for direct air capture of CO2via deep reinforcement learning

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Sorbent are the workhorses of most contemporary separation processes. Crystalline sorbents boast descript structures that facilitate in silico study. Their diversity is matched by their varied applications. At the forefront of their currently researched ap ...

Industries with low-temperature heat demand, such as laundry and syrup sectors, heavily rely on natural gas-fired boilers, posing challenges to achieving net-zero emissions by 2050. Like hard-to-abate sectors, they must explore energy transition strategies ...

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

Aluminium is recognized as an essential material for the global energy transition. However, its production is extremely energy-intensive and largely dependent on fossil fuels. The aluminium industry emits more than 1.11 Gt CO2-eq annually. Multiple obstacl ...

This paper investigates the fundamental regression task of learning k neurons (a.k.a. teachers) from Gaussian input, using two-layer ReLU neural networks with width m (a.k.a. students) and m, k = O(1), trained via gradient descent under proper initializati ...

Technologies for the transformation of atmospheric CO2 to useful chemicals, such as formic acid (FA), are essential to combatting excessive fossil fuel use and will need to be implemented on large scale. However, hydrogenation of CO2 to (base-free) FA is c ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

Deep neural networks (DNNs) are receiving increasing attention in wind power forecasting due to their ability to effectively capture complex patterns in wind data. However, their forecast errors are severely limited by the local optimal weight issue in opt ...

MOFs are a class of porous crystalline materials whose unique properties have led to applicability in several fields ranging from gas adsorption to drug delivery. Despite their high potential, MOFs are usually found as fine powders, a property that can lim ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

This dataset contains a collection of ultrafast ultrasound acquisitions from nine volunteers and the CIRS 054G phantom. For a comprehensive understanding of the dataset, please refer to the paper: Viñals, R.; Thiran, J.-P. A KL Divergence-Based Loss for In ...

Driven by the need for more efficient and seamless integration of physical models and data, physics -informed neural networks (PINNs) have seen a surge of interest in recent years. However, ensuring the reliability of their convergence and accuracy remains ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30% of the global energy consumption. With traditional engineering solutions reaching their limits to tackle such large-scale problems, data-driven methods and Machine ...

Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the prediction error is an important component that compares the expected and actual rewards. Dopamine plays a critical role in encoding these prediction errors. In my ...

This work presents a recommendation system for metal-organic frameworks (MOFs) inspired by online content platforms. By leveraging the unsupervised Doc2Vec model trained on document-structured intrinsic MOF characteristics, the model embeds MOFs into a hig ...

Inverse design of metal-organic frameworks for direct air capture of CO2via deep reinforcement learning

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Harnessing modular approaches in the study of crystalline sorbents

Evaluation of Energy Transition Pathways for Industries with Low-Temperature Heat Demand: The Case of Laundry and Syrup Sectors

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Decarbonizing the aluminium industry: A comprehensive review of pathways and process integration perspectives

How Gradient Descent Balances Features: A Dynamical Analysis For Two-Layer Neural Networks

Single-site ruthenium catalyst supported on zeolite for CO2 hydrogenation to methyl formate

Understanding generalization and robustness in modern deep learning

Improving the accuracy and interpretability of neural networks for wind power forecasting

Densification and shaping of pure Cu-BTC powders using a solid-state chemical transformation

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (2/6)

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (3/6)

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (4/6)

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (5/6)

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning: Dataset (6/6)

Residual-based attention in physics-informed neural networks

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Unveiling the complexity of learning and decision-making

Deep learning-based recommendation system for metal-organic frameworks (MOFs)