From the 4 of 158 papers with an AI index.
37 citations
- Istituto Nazionale di Fisica Nucleare, Sezione di BolognaIT72 papers
- Université Paris-SaclayFR72 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di PadovaIT71 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di Roma IIT71 papers
- University of ZurichCH71 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di GenovaIT70 papers
- Massachusetts Institute of TechnologyUS70 papers
- University of BristolGB69 papers
- University of Maryland, College ParkUS68 papers
- The Ohio State UniversityUS67 papers
- Rutherford Appleton LaboratoryGB66 papers
- Istituto Nazionale di Fisica Nucleare, Sezione di BariIT65 papers
10 papers · 1 filter
MatBind: A Shared Embedding Space for Multimodal Materials Characterization
Le Yang, Anoop K. Chandran, Jona Ãstreicher +8
Fully characterizing a crystalline material requires integrating heterogeneous data sources -- atomic structures, diffraction patterns, electronic density of states, and natural la…
Does Dimensionality Reduction via Random Projections Preserve Landscape Features?
Iván Olarte RodrÃguez, Anja Jankovic, Thomas Bäck +1
Exploratory Landscape Analysis (ELA) provides numerical features for characterizing black-box optimization problems. In high-dimensional settings, however, ELA suffers from sparsit…
Framework for Grouping Local Process Models
Viki Peeva, Wil M. P. van der Aalst
Local Process Models (LPMs) are an underexplored concept in process mining. LPMs describe patterns in event data considering sequence, choice, concurrency, and loop. In recent year…
MiniFool -- Physics-Constraint-Aware Minimizer-Based Adversarial Attacks in Deep Neural Networks
Lucie Flek, Oliver Janik, Philipp Alexander Jung +8
In this paper, we present a new algorithm, MiniFool, that implements physics-inspired adversarial attacks for testing neural network-based classification tasks in particle and astr…
Differentiable Thermodynamic Phase-Equilibria for Machine Learning
Karim K. Ben Hicham, Moreno Ascani, Jan G. Rittig +1
Accurate prediction of phase equilibria remains a central challenge in chemical engineering. Physics-consistent machine learning methods that incorporate thermodynamic structure in…
Capabilities of Auto-encoders and Principal Component Analysis of the Reduction of Microstructural Images; Application on the Acceleration of Phase-Field Simulations
Seifallah Fetni, Thinh Quy Duc Pham, Truong Vinh Hoang +4
In this work, a data-driven framework based on Phase-Field simulations data is proposed to highlight the capabilities of neural networks to ensure accurate low dimensionality reduc…