Bayesian Machine Learning Approach to the Quantification of Uncertainties on Ab Initio Potential Energy Surfaces S Venturi, RL Jaffe, M Panesi
The Journal of Physical Chemistry A 124 (25), 5129–5146, 2020
60 2020 Data-Inspired and Physics-Driven Model Reduction for Dissociation: Application to the O2+O System S Venturi, MP Sharma, B Lopez, M Panesi
The Journal of Physical Chemistry A 124 (41), 8359-8372, 2020
51 2020 SVD Perspectives for Augmenting DeepONet Flexibility and Interpretability S Venturi, T Casey
Computer Methods in Applied Mechanics and Engineering 403, 115718, 2023
40 2023 Comparison of Potential Energy Surface and Computed Rate Coefficients for RL Jaffe, M Grover, S Venturi, DW Schwenke, P Valentini, ...
Journal of thermophysics and heat transfer 32 (4), 869-881, 2018
39 2018 Rovibrational-Specific QCT and Master Equation Study on N2 (X1 Σg + ) + O(3 P) and NO(X2 Π) + N(4 S) Systems in High-Energy Collisions SM Jo, S Venturi, MP Sharma, A Munafò, M Panesi
The Journal of Physical Chemistry A 126 (21), 3273-3290, 2022
31 2022 Comparison of quantum mechanical and empirical potential energy surfaces and computed rate coefficients for N2 dissociation RL Jaffe, DW Schwenke, M Grover, P Valentini, TE Schwartzentruber, ...
54th AIAA Aerospace Sciences Meeting, 0503, 2016
26 2016 Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows I Zanardi, S Venturi, M Panesi
Scientific reports 13 (1), 15497, 2023
18 2023 Calibration and uncertainty quantification of vista ablator material database using bayesian inference P Rostkowski, S Venturi, M Panesi, A Omidy, H Weng, A Martin
Journal of Thermophysics and Heat Transfer 33 (2), 356-369, 2019
16 2019 Rovibrational internal energy transfer and dissociation of high-temperature oxygen mixture SM Jo, S Venturi, JG Kim, M Panesi
The Journal of Chemical Physics 158 (6), 2023
15 2023 Application of DeepOnet to model inelastic scattering probabilities in air mixtures M Sharma Priyadarshini, S Venturi, M Panesi
AIAA Aviation 2021 Forum, 3144, 2021
15 2021 Towards efficient simulations of non-equilibrium chemistry in hypersonic flows: a physics-informed neural network framework I Zanardi, S Venturi, M Panesi
AIAA SCITECH 2022 Forum, 1639, 2022
12 2022 Effects of Ab-Initio Potential Energy Surfaces on O2-O Non-Equilibrium Kinetics S Venturi, M Sharma Priyadarshini, A Racca, M Panesi
AIAA Aviation 2019, 2019
11 2019 A Machine Learning Framework for the Quantification of the Uncertainties Associated with Ab-Initio Based Modeling of Non-Equilibrium Flows S Venturi, M Sharma Priyadarshini, M Panesi
AIAA Scitech 2019 Forum, 0788, 2019
11 2019 Physics-based stochastic framework for the quantification of uncertainty in non-equilibrium hypersonic flows S VENTURI
Politecnico di Milano, 2014
10 2014 Comprehensive study of HCN: Potential energy surfaces, state-to-state kinetics, and master equation analysis MS Priyadarshini, SM Jo, S Venturi, DW Schwenke, RL Jaffe, M Panesi
The Journal of Physical Chemistry A 126 (44), 8249-8265, 2022
8 2022 Machine Learning and Uncertainty Quantification Framework for Predictive Ab Initio Hypersonics S Venturi
University of Illinois at Urbana-Champaign, 2021
8 2021 State-to-state and reduced-order models for recombination and energy transfer in aerothermal environments A Munafò, S Venturi, RL Macdonald, M Panesi
54th AIAA Aerospace Sciences Meeting, 0505, 2016
7 2016 Efficient quasi-classical trajectory calculations by means of neural operator architectures MS Priyadarshini, S Venturi, I Zanardi, M Panesi
Physical Chemistry Chemical Physics 25 (20), 13902-13912, 2023
6 2023 Reduced-order modeling for non-equilibrium air flows A Munafò, S Venturi, M Sharma Priyadarshini, M Panesi
AIAA Scitech 2020 Forum, 1226, 2020
6 2020 Investigating CO dissociation by means of coarse grained ab-initio rate constants S Venturi, M Panesi
2018 AIAA Aerospace Sciences Meeting, 1232, 2018
5 2018 
Marco PanesiProfessor of Aerospace Engineering, University of Illinois at Urbana-Champaignبريد إلكتروني تم التحقق منه على illinois.edu
