We develop innovative approaches for stochastic adjoint methods
We combine in-depth engineering insight with the most modern computational technologies
We have a wide experience on cloud and HPC systems for data storage and computation
We build new methodologies Monte Carlo analysis and Perturbation Theory
We create FV and FEM tools for the analysis of the most challenging multi-physics applications
Adopting ensemble weather forecast we deliver the most reliable energy predictions
We use Bayesian Machine Learning for robust Uncertainty Quantification
We develop advanced photon and neutron transport algorithms
Reduced Order Models drive us towards accuracy and computational efficiency
We have large experience in the development of high-quality, low-maintenance software
We provide innovative, customized tools and services to model your multiphysic system or component, combining CFD, solid mechanics, heat transfer, particle transport, etc.
We combine our wide experience in system engineering with our knowledge in computational science to support the design and operation of complex engineering systems
We support our customers in the creation of digital twins of their systems to set up testing and experimental campaigns, optimize operation, and accelerate innovation
We employ our skills in machine learning, data science, reduced order modeling, and stochastic modeling to provide our customers with cutting edge predictive models for various applications
MMP is supporting IGS in the development of a data-driven gas storage product optimizer.
We are developing a revolutionary, sustainable, offshore energy storage.
On 15th January 2019, MMP has been selected in the Call for Growth by Next Energy (Cariplo Factory) to access the Engage Phase and start a collaborative service development and…
MMP works on reactivity evolution of fuel salt during its draining.
We study the behavior of the ThorCon MSR during salt draining through the freeze valves.
We work on the heat transfer between the drain tanks and the surrounding structure for the ThorCon MSR reactor.
Lead-cooled Fast Reactors (LFRs) are studied in the frame of the OCAPIE project, coordinated by the Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Genova. The purpose of this work…
We developed a Machine Learning system capable of estimating statistical inflows in the hydroelectric power plants of the whole European Union.
We have been selected by ENTSO-E to develop an electric load forecasting tool both mathematically advanced and easy to use: we tackled this task by developing TRAPUNTA.
