Simone Venturi

发表

A Bayesian Machine Learning Approach to the Quantification of Uncertainties on Ab Initio Potential Energy Surfaces.

R. Jaffe, S. Venturi, M. Panesi, 2020, The journal of physical chemistry. A.

Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows

S. Venturi, M. Panesi, Ivan Zanardi, 2022, Scientific reports.

A Data-Inspired and Physics-Driven Model Reduction for Dissociation: Application to O2+O System.

S. Venturi, M. Panesi, M. P. Sharma, 2020, The journal of physical chemistry. A.

Calibration and Uncertainty Quantification of VISTA Ablator Material Database Using Bayesian Inference

Ali D. Omidy, S. Venturi, M. Panesi, 2019, Journal of Thermophysics and Heat Transfer.

Physics-driven modeling for aerothermodynamics

S. Venturi, Maitreyee Sharma Priyadarshini, S. Jo, 2021, AIAA AVIATION 2021 FORUM.

A Machine Learning Framework for the Quantification of the Uncertainties Associated with Ab-Initio Based Modeling of Non-Equilibrium Flows

M. Panesi, M. Priyadarshini, Simone Venturi, 2019, AIAA Scitech 2019 Forum.

Towards Efficient Simulations of Non-Equilibrium Chemistry in Hypersonic Flows: Application of Neural Operators in Multidimensional CFD Simulations

Ivan Zanardi, Simone Venturi, M. Panesi, 2024, AIAA SCITECH 2024 Forum.

Towards Efficient Simulations of Non-Equilibrium Chemistry in Hypersonic Flows: Application of Physics-Informed DeepONet to Shock-Heated Flow Scenarios

S. Venturi, M. Panesi, Ivan Zanardi, 2023, AIAA SCITECH 2023 Forum.

Comparison of quantum mechanical and empirical potential energy surfaces and computed rate coefficients for N2 dissociation

R. Jaffe, S. Venturi, M. Panesi, 2016 .

Rovibrational-Specific QCT and Master Equation Study on N2(X1Σg+) + O(3P) and NO(X2Π) + N(4S) Systems in High-Energy Collisions.

M. Panesi, Maitreyee P. Sharma, S. Jo, 2021, The journal of physical chemistry. A.