Search Results for author:
Found 40 papers, 7 papers with code
ML4PhySim : Machine Learning for Physical Simulations Challenge (The airfoil design)
The aim of this competition is to encourage the development of new ML techniques to solve physical problems using a unified evaluation framework proposed recently, called Learning Industrial Physical Simulations (LIPS).
Multi-Level GNN Preconditioner for Solving Large Scale Problems
The proposed GNN-based preconditioner is used to enhance the efficiency of a Krylov method, resulting in a hybrid solver that can converge with any desired level of accuracy.
Interpretable learning of effective dynamics for multiscale systems
In recent years, techniques based on deep recurrent neural networks have produced promising results for the modeling and simulation of complex spatiotemporal systems and offer large flexibility in model development as they can incorporate experimental and computational data.
Meta-Learning for Airflow Simulations with Graph Neural Networks
To this end, we present a meta-learning approach to enhance the performance of learned models on OoD samples.
MultiZenoTravel: a Tunable Benchmark for Multi-Objective Planning with Known Pareto Front
Multi-objective AI planning suffers from a lack of benchmarks exhibiting known Pareto Fronts.
An Implicit GNN Solver for Poisson-like problems
This paper presents $\Psi$-GNN, a novel Graph Neural Network (GNN) approach for solving the ubiquitous Poisson PDE problems with mixed boundary conditions.
Continuous Methods : Adaptively intrusive reduced order model closure
Reduced order modeling methods are often used as a mean to reduce simulation costs in industrial applications.
DS-GPS : A Deep Statistical Graph Poisson Solver (for faster CFD simulations)
This paper proposes a novel Machine Learning-based approach to solve a Poisson problem with mixed boundary conditions.
Continuous Methods : Hamiltonian Domain Translation
This paper proposes a novel approach to domain translation.
CD-ROM: Complemented Deep-Reduced Order Model
Model order reduction through the POD-Galerkin method can lead to dramatic gains in terms of computational efficiency in solving physical problems.
Frugal Machine Learning
Machine learning, already at the core of increasingly many systems and applications, is set to become even more ubiquitous with the rapid rise of wearable devices and the Internet of Things.
Learning meta-features for AutoML
This paper tackles the AutoML problem, aimed to automatically select an ML algorithm and its hyper-parameter configuration most appropriate to the dataset at hand.
DISCO Verification: Division of Input Space into COnvex polytopes for neural network verification
The impressive results of modern neural networks partly come from their non linear behaviour.
Paradiseo: From a Modular Framework for Evolutionary Computation to the Automated Design of Metaheuristics ---22 Years of Paradiseo---
The success of metaheuristic optimization methods has led to the development of a large variety of algorithm paradigms.
Zoetrope Genetic Programming for Regression
The Zoetrope Genetic Programming (ZGP) algorithm is based on an original representation for mathematical expressions, targeting evolutionary symbolic regression. The zoetropic representation uses repeated fusion operations between partial expressions, starting from the terminal set.
Deep Statistical Solvers
This paper introduces Deep Statistical Solvers (DSS), a new class of trainable solvers for optimization problems, arising e. g., from system simulations.
CAMUS: A Framework to Build Formal Specifications for Deep Perception Systems Using Simulators
We propose to take advantage of the simulators often used either to train machine learning models or to check them with statistical tests, a growing trend in industry.
LEAP nets for power grid perturbations
We propose a novel neural network embedding approach to model power transmission grids, in which high voltage lines are disconnected and reconnected with one-another from time to time, either accidentally or willfully.
Automated Machine Learning with Monte-Carlo Tree Search
The AutoML task consists of selecting the proper algorithm in a machine learning portfolio, and its hyperparameter values, in order to deliver the best performance on the dataset at hand.
Multi-Domain Adversarial Learning
Multi-domain learning (MDL) aims at obtaining a model with minimal average risk across multiple domains.
On the Behaviour of Differential Evolution for Problems with Dynamic Linear Constraints
Our proposed framework creates dynamic benchmarks that are flexible in terms of number of changes, dimension of the problem and can be applied to test any objective function.
Optimization of computational budget for power system risk assessment
We address the problem of maintaining high voltage power transmission networks in security at all time, namely anticipating exceeding of thermal limit for eventual single line disconnection (whatever its cause may be) by running slow, but accurate, physical grid simulators.
Anticipating contingengies in power grids using fast neural net screening
We evaluate that our method scales up to power grids of the size of the French high voltage power grid (over 1000 power lines).
The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities
Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them.
Fast Power system security analysis with Guided Dropout
We propose a new method to efficiently compute load-flows (the steady-state of the power-grid for given productions, consumptions and grid topology), substituting conventional simulators based on differential equation solvers.
Introducing machine learning for power system operation support
One of the primary goals of dispatchers is to protect equipment (e. g. avoid that transmission lines overheat) with few degrees of freedom: we are considering in this paper solely modifications in network topology, i. e. re-configuring the way in which lines, transformers, productions and loads are connected in sub-stations.
Stochastic Gradient Descent: Going As Fast As Possible But Not Faster
When applied to training deep neural networks, stochastic gradient descent (SGD) often incurs steady progression phases, interrupted by catastrophic episodes in which loss and gradient norm explode.
Toward Optimal Run Racing: Application to Deep Learning Calibration
This paper aims at one-shot learning of deep neural nets, where a highly parallel setting is considered to address the algorithm calibration problem - selecting the best neural architecture and learning hyper-parameter values depending on the dataset at hand.
Anomaly Detection with the Voronoi Diagram Evolutionary Algorithm
This paper presents the Voronoi diagram-based evolutionary algorithm (VorEAl).
Racing Multi-Objective Selection Probabilities
In the context of Noisy Multi-Objective Optimization, dealing with uncertainties requires the decision maker to define some preferences about how to handle them, through some statistics (e. g., mean, median) to be used to evaluate the qualities of the solutions, and define the corresponding Pareto set.
Maximum Likelihood-based Online Adaptation of Hyper-parameters in CMA-ES
The Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is widely accepted as a robust derivative-free continuous optimization algorithm for non-linear and non-convex optimization problems.
Computational Methods for Probabilistic Inference of Sector Congestion in Air Traffic Management
This article addresses the issue of computing the expected cost functions from a probabilistic model of the air traffic flow and capacity management.
Multiobjective Tactical Planning under Uncertainty for Air Traffic Flow and Capacity Management
We investigate a method to deal with congestion of sectors and delays in the tactical phase of air traffic flow and capacity management.
Strategic Planning in Air Traffic Control as a Multi-objective Stochastic Optimization Problem
Since air traffic regulations and sector congestion are antagonist, we designed and implemented a multi-objective optimization algorithm for determining the best trade-off between these two criteria.
KL-based Control of the Learning Schedule for Surrogate Black-Box Optimization
This weakness is commonly addressed through surrogate optimization, learning an estimate of the objective function a. k. a.
Quality Measures of Parameter Tuning for Aggregated Multi-Objective Temporal Planning
Several meta-optimization methods have been proposed to find the best parameter set for a given optimization algorithm and (set of) problem instances.
Multi-Objective AI Planning: Comparing Aggregation and Pareto Approaches
Most real-world Planning problems are multi-objective, trying to minimize both the makespan of the solution plan, and some cost of the actions involved in the plan.
Sustainable Cooperative Coevolution with a Multi-Armed Bandit
At each iteration, the dynamic multi-armed bandit makes a decision on which species to evolve for a generation, using the history of progress made by the different species to guide the decisions.
APRIL: Active Preference-learning based Reinforcement Learning
This paper focuses on reinforcement learning (RL) with limited prior knowledge.
Self-Adaptive Surrogate-Assisted Covariance Matrix Adaptation Evolution Strategy
The resulting algorithm, saACM-ES, adjusts online the lifelength of the current surrogate model (the number of CMA-ES generations before learning a new surrogate) and the surrogate hyper-parameters.
