Deep excavation of the impact from endogenous and exogenous uncertainties on long-term energy planning

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Atomistic machine learning has revolutionized atomistic modeling. Traditionally, molecular dynamics simulations relied on classical force fields or electronic structure calculations like Density Functional Theory. Classical force fields are fast but less p ...

Living systems are commonly engineered through an iterative design-build-test process. However, unlike in other engineering disciplines, the systems that synthetic biologists construct are able to evolve. On the one hand, this compromises their long-term s ...

Recent years have witnessed a growing integration of automatic control into a wide range of cyber-physical systems, including electrical grids and autonomous transportation networks. These systems operate in uncertain and dynamic environments, exhibit comp ...

Federated Learning (FL) enables end-user devices to collaboratively train ML models without sharing raw data, thereby preserving data privacy. In FL, a central parameter server coordinates the learning process by iteratively aggregating the trained models ...

The aluminium sector relies on natural gas for the conversion of recycled scrap into new feedstock, which results in substantial atmospheric emissions. Hydric resources are also impacted, as they serve as heat sinks for the waste heat generated during the ...

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 ...

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 ...

Machine learning algorithms such as Convolutional Neural Networks (CNNs) are characterized by high robustness towards quantization, supporting small-bitwidth fixed-point arithmetic at inference time with little to no degradation in accuracy. In turn, small ...

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 ...

AIM: To characterise the corticoreticular pathway (CRP) in a case -control cohort of adolescent idiopathic scoliosis (AIS) patients using high -resolution slice -accelerated readoutsegmented echo -planar diffusion tensor imaging (DTI) to enhance the discri ...

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

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 ...

In Part I of this paper, an inequality satisfied by the vacuum energy density of the universe was derived using an indirect and heuristic procedure. The derivation is based on a proposed thought experiment, according to which an electron is accelerated to ...

Industrial chemistry heavily relies on traditional separation methods which are both energy-demanding and environmentally detrimental. This thesis addresses critical separation challenges, specifically carbon capture applications and the separation of ethy ...

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

Healthcare sector buildings, particularly hospitals, significantly contribute to global energy consumption and greenhouse gas emissions. Hospitals require a substantial amount of steam, including the essential hygienic or " clean steam, " that is currently ...

The European Union's Green Deal aims for a 55% reduction in greenhouse gas emissions by 2030. To reach this goal, a massive integration of Renewable Energy Sources (RES) into the power grid is necessary. As RES become a large part of the electricity genera ...

Context. The Euclid mission of the European Space Agency will perform a survey of weak lensing cosmic shear and galaxy clustering in order to constrain cosmological models and fundamental physics. Aims. We expand and adjust the mock Euclid likelihoods of t ...

The increasing availability of multimodal data from electronic health records (EHR) has paved the way for deep learning methods to improve diagnosis accuracy. However, deep learning models are data-driven, requiring large-scale datasets to achieve high gen ...

Deep excavation of the impact from endogenous and exogenous uncertainties on long-term energy planning

Chat with Graph Search

Advancing understanding and practical performance of machine learning interatomic potentials

Light-directed evolution of dynamic biological systems

Optimal Control under Uncertainty: From Regret Minimization to Distributional Robustness

Practical Federated Learning without a Server

Renewable energy integration and waste heat valorization in aluminum remelting for co-producing kerosene and methanol

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

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

DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD

CO2 Capture and Management Strategies for Decarbonizing Secondary Aluminium Production

The hindbrain and cortico-reticular pathway in adolescent idiopathic scoliosis

Partial discharge localization in power transformer tanks using machine learning methods

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

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

Heuristic Estimation of the Vacuum Energy Density of the Universe: Part II-Analysis Based on Frequency Domain Electromagnetic Radiation

Harnessing Porous Materials for Efficient Gas Capture: A Computational Exploration of Structure, Function, and Workflow Automation

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Solar-assisted clean steam generator, a cleaner production approach for sustainable healthcare facilities

Advanced control strategies to exploit the hydropower potential enhancing ancillary services provision to the power system

Euclid: Validation of the MontePython forecasting tools

CARER - ClinicAl Reasoning-Enhanced Representation for Temporal Health Risk Prediction