Publications associées à Process-informed adsorbent design guidelines for direct air capture

Publications associées (53)

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Recent progress in computer vision has been driven by a simple yet transformative observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...

EPFL2025

A diversity-driven discovery of potential nanoporous materials for energy-related applications

Sauradeep Majumdar

The design and discovery of potential novel materials is critical for the advancement of climate change mitigation technologies. In this respect, metal-organic frameworks (MOFs) have received considerable attention over the last two decades. The combinatio ...

EPFL2024

Modelleyen: Continual Learning and Planning via Structured Modelling of Environment Dynamics

Boi Faltings, Zeki Doruk Erden

The current machine learning paradigm relies on continuous representations and fixed neural network architectures to approximate environmental structures, leading to challenges with continual learning, internal structure design, and goal-directed behavior ...

Springer Science and Business Media Deutschland GmbH2025

CO2 Capture and Management Strategies for Decarbonizing Secondary Aluminium Production

François Maréchal, Daniel Alexander Florez Orrego, Meire Ellen Gorete Ribeiro Domingos, Dareen Zuhir Omar Dardor

The production of aluminium largely depends on the use of fossil fuels, resulting in the emission of significant amounts of greenhouse gases. As the aluminium industry is working towards decreasing its environmental burdens, the elimination of direct emiss ...

2024

Embodied Carbon Premium for Cantilevers

David Michael Volpe Ruggiero, Paola Costanza Miglietta

This study addresses the critical need for sustainable architectural designs within the context of climate change and the significant role the built environment plays in greenhouse gas emissions. The focus of this paper is on understanding the influence of ...

2024

A robust asymptotic control model to analyze climate policy with CDR options

Marc Vielle

A three-region optimal economic growth model is proposed to represent the global energy transition to net-zero emissions when carbon dioxide removal (CDR) technologies are available. The main features of the model are (i) the representation of the economy ...

2025

The Effect of Rivers on Energy Balance at the Neighborhood Scale. A comparative analysis of four Rhodanian sites in France and Switzerland

Emmanuel Rey, Sergi Aguacil Moreno, Martine Laprise, Sara Sonia Formery Regazzoni

This research aims to compare the greenhouse gas (GHG) emissions of new neighbourhood projects near the Rhône River in France and Switzerland. The study considers the influence of the river on energy demand and resilience to climate change of 12 project-ba ...

Wrocław University of Science and Technology Publishing House2024

Learning to Reason with Neural Networks: Principles and Methods

Aryo Lotfi Jandaghi

This thesis focuses on understanding and improving the reasoning capabilities of neural networks. It develops theoretical results and empirical analyses to uncover reasoning potential and limitations, leveraging these insights to guide the design of improv ...

EPFL2025

A comparative analysis of gene expression profiling by statistical and machine learning approaches

Anaïs Betsabeh Haget, Maria Boulougouri

Motivation Many machine learning (ML) models developed to classify phenotype from gene expression data provide interpretations for their decisions, with the aim of understanding biological processes. For many models, including neural networks, interpretati ...

OXFORD UNIV PRESS2025

Spatio-temporal patterns and drivers of CH4 and CO2 fluxes from rivers and lakes in highly urbanized areas

Alexandre Buttler

Gaseous carbon exchange at the water-air interface of rivers and lakes is an essential process for regional and global carbon cycle assessments. Many studies have shown that rivers surrounding urban landscapes can be hotspots for greenhouse gas (GHG) emiss ...

Elsevier2024

Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy

Fernando Porté Agel, Jiannong Fang, Wenlong Liao

Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box model that combines high accurac ...

2025

An explorative method to support design decisions based on carbon constraints and daylight sufficiency needs

Nazanin Rezaei Oghazi

The global construction industry contributes to 37% of carbon emissions associated to both building operations and construction. To help achieve the net-zero targets set by 2050, it is mandated to achieve a 50% reduction in carbon emissions by 2030. As we ...

EPFL2024

Spectroscopy of two-dimensional interacting lattice electrons using symmetry-aware neural backflow transformations

Giuseppe Carleo, Jannes Willy E. Nys, Imelda Romero

Neural networks have shown to be a powerful tool to represent the ground state of quantum many-body systems, including fermionic systems. However, efficiently integrating lattice symmetries into neural representations remains a significant challenge. In th ...

NATURE PORTFOLIO2025

Impact of anthropogenic emission control in reducing future PM2.5 concentrations and the related oxidative potential across different regions of China

Athanasios Nenes, Yuan Yuan

Affected by both future anthropogenic emissions and climate change, future prediction of PM2.5 and its Oxidative Potential (OP) distribution is a significant challenge, especially in developing countries like China. To overcome this challenge, we estimated ...

Elsevier2024

Computational design of complex protein folds and soluble analogs of membrane proteins

Casper Alexander Goverde

Proteins are the molecular machines of life, driving essential biological processes such as enzymatic catalysis, cellular signaling, and immune responses. The ability to design entirely new proteins with desired structures and functions has immense implica ...

EPFL2025

Tailoring nanocatalysts and reaction interfaces for water- and CO2-electrolysis

Thi Ha My Pham

Renewable energy sources offer a promising solution for mitigating sustainability and CO2 emissions-related issues due to their vast energy generation capacity. They enable hydrogen production via water electrolysis, as well as carbon capture and utilizati ...

EPFL2024

LIONHEART: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles

David Atienza Alonso, Giovanni Ansaloni, Marina Zapater Sancho, Irem Boybat Kara, Flavio Ponzina, Joshua Alexander Harrison Klein, Yuxuan Wang, Abu Sebastian

When arranged in a crossbar configuration, resistive memory devices can be used to execute Matrix-Vector Multiplications (MVMs), the most dominant operation of many Machine Learning (ML) algorithms, in constant time complexity. Nonetheless, when performing ...

Institute of Electrical and Electronics Engineers (IEEE)2025

The impacts of decarbonization pathways on Sustainable Development Goals in the European Union

Marc Vielle, Sigit Pria Perdana

Climate action to achieve the Paris Agreement should respect the United Nations Sustainable Development Goals. Here, we use an integrated assessment modelling framework comprising nine climate policy models and quantify the impacts of decarbonisation pathw ...

2024

Power for AI and AI for Power: The Infinite Entanglement Between Artificial Intelligence and Power Electronics Systems

Olga Fink

This article summarizes the discussions and outcomes of the "Power for AI and AI for Power" session at the IEEE Power Electronics Society (PELS) Future of Electronic Power Processing and Conversion (FEPPCON XII). Power electronics embeds intelligence into ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2025

Overcoming Distribution Shifts and Imbalance Challenges in Representation Learning for Deep Regression Models

Ismail Nejjar

Deep learning has shown remarkable potential for industrial applications, particularly in predictive maintenance and condition monitoring. A key challenge in this domain is prognostics, specifically predicting the Remaining Useful Life (RUL) of assets - a ...

EPFL2025