Name: Roberto Rocchetta
Type: User
Company: SUPSI
Bio: Resilience, reliability, risk, vulnerability models and ML+statistical approaches for uncertainty quantification for energy systems, grids, and black boxes.
Twitter: Roberto27118657
Location: Mendrisio, CH
Blog: https://roberock.github.io/
Roberto Rocchetta's Projects
TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.
Bayesian Framework for Updating
Extension of the ISIPTA paper 2021 (Constructing consonant predictive beliefs from data with scenario theory)
Reliability based design optimization of a non-linear controller
A database of anonimiyed degradation(longitudinal) and reliability data from several light-edmitting diodes packages and lamps, obtained from accelerated stress tests like IES LM-80ies
Grid Aware Mobility and Energy Sharing: GAMES
Heat and electric grid simulation and resilience analysis
A latex package for displaying Julia code using the listings package. The package supports pdftex, luatex and xetex for compilation.
Distribution System Simulator based on OpenDSS and OpenDSSDirect.py. Modern Syntax, DataFrames, Pint, Networkx, Algorithmic Agents.
Markdown fenced code block syntax highlighting for Julia
Include markdown files as Julia source code.
Solve A MObjective Optimization using local sensitivity to speed up the ofsprings fittness calculation
Topological Vulnerability Metrics, Contingency Ranking, Spectral Graph Anlaysis, Spectral Island Clustering
Reinforcement learning environment for optimal EV relocation and fleet rebalancing in free floating carsharing systems
Usage examples, demos, and tutorials for OSMnx.
:pencil: A program that adds LaTeX math to README.md files (Offline - in repo image hosting) :pencil2:
reinforcement learning for power grid optimal operations and maintenance
Webpage html
Methods to compute generalization error bounds using Scenario Theory
Different Training Schemes for Interval Predictor Model and Generalization Bounds on the reliability of their predictions
A set of methods and proprieties to perform reliability-based-design-optimization by Scenario theory. Scenario optimization makes direct use of the available data (the uncertain parameters delta) thereby eliminating the need for estimating the distribution of the uncertain parameters
Calculate the real cost to run your JS app or lib to keep good performance. Show error in pull request if the cost exceeds the limit.
Code for computing the stochastic area metric in Python and Matlab.
notebooks with teaching material