Generation of Optimal Artificial Neural Networks Using a Pattern Search Algorithm: Application to Approximation of Chemical Systems. - Matthias Ihme; Alison L. Marsden; Heinz Pitsch (Neural Comput., 2008)

Regularization of reaction progress variable for application to flamelet-based combustion models. - Matthias Ihme; Lee Shunn; Jian Zhang (J. Comput. Phys., 2012)

Discontinuous Galerkin method for multicomponent chemically reacting flows and combustion. - Yu Lv; Matthias Ihme (J. Comput. Phys., 2014)

Entropy-bounded discontinuous Galerkin scheme for Euler equations. - Yu Lv; Matthias Ihme (J. Comput. Phys., 2015)

An entropy-residual shock detector for solving conservation laws using high-order discontinuous Galerkin methods. - Yu Lv; Yee Chee See; Matthias Ihme (J. Comput. Phys., 2016)

An entropy-stable hybrid scheme for simulations of transcritical real-fluid flows. - Peter C. Ma; Yu Lv; Matthias Ihme (J. Comput. Phys., 2017)

Efficient time-stepping techniques for simulating turbulent reactive flows with stiff chemistry. - Hao Wu 069; Peter C. Ma; Matthias Ihme (Comput. Phys. Commun., 2019)

Shock capturing for discontinuous Galerkin methods with application to predicting heat transfer in hypersonic flows. - Eric J. Ching; Yu Lv; Peter A. Gnoffo; Michael Barnhardt; Matthias Ihme (J. Comput. Phys., 2019)

Data-assisted combustion simulations with dynamic submodel assignment using random forests. - Wai Tong Chung; Aashwin Ananda Mishra; Nikolaos Perakis; Matthias Ihme (CoRR, 2020)

Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data. - Fantine Huot; R. Lily Hu; Matthias Ihme; Qing Wang 032; John Burge; Tianjian Lu; Jason Hickey; Yi-Fan Chen; John R. Anderson (CoRR, 2020)

Convolutional LSTM Neural Networks for Modeling Wildland Fire Dynamics. - John Burge; Matthew Bonanni; Matthias Ihme; R. Lily Hu (CoRR, 2020)

A two-way coupled Euler-Lagrange method for simulating multiphase flows with discontinuous Galerkin schemes on arbitrary curved elements. - Eric J. Ching; Steven R. Brill; Michael Barnhardt; Matthias Ihme (J. Comput. Phys., 2020)

Accelerating High-fidelity Combustion Simulations with Classification Algorithms. - Wai Tong Chung; Aashwin Ananda Mishra; Nikolaos Perakis; Matthias Ihme (AAAI Spring Symposium: MLPS, 2021)

Interpretable Data-driven Methods for Subgrid-scale Closure in LES for Transcritical LOX/GCH4 Combustion. - Wai Tong Chung; Aashwin Ananda Mishra; Matthias Ihme (CoRR, 2021)

Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data. - Fantine Huot; R. Lily Hu; Nita Goyal; Tharun Sankar; Matthias Ihme; Yi-Fan Chen (CoRR, 2021)

Efficient projection kernels for discontinuous Galerkin simulations of disperse multiphase flows on arbitrary curved elements. - Eric J. Ching; Matthias Ihme (J. Comput. Phys., 2021)

Development of a particle collision algorithm for discontinuous Galerkin simulations of compressible multiphase flows. - Eric J. Ching; Matthias Ihme (J. Comput. Phys., 2021)

The Bearable Lightness of Big Data: Towards Massive Public Datasets in Scientific Machine Learning. - Wai Tong Chung; Ki Sung Jung; Jacqueline H. Chen; Matthias Ihme (CoRR, 2022)

Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior. - John Burge; Matthew R. Bonanni; R. Lily Hu; Matthias Ihme (CoRR, 2022)

A TensorFlow simulation framework for scientific computing of fluid flows on tensor processing units. - Qing Wang 032; Matthias Ihme; Yi-Fan Chen; John R. Anderson (Comput. Phys. Commun., 2022)

Quail: A lightweight open-source discontinuous Galerkin code in Python for teaching and prototyping. - Eric J. Ching; Brett Bornhoft; Ali Lasemi; Matthias Ihme (SoftwareX, 2022)

Towards Data-Informed Motion Artifact Reduction in Quantitative CT Using Piecewise Linear Interpolation. - Emeric Boigné; Dilworth Y. Parkinson; Matthias Ihme (IEEE Trans. Computational Imaging, 2022)

Next Day Wildfire Spread: A Machine Learning Dataset to Predict Wildfire Spreading From Remote-Sensing Data. - Fantine Huot; R. Lily Hu; Nita Goyal; Tharun Sankar; Matthias Ihme; Yi-Fan Chen (IEEE Trans. Geosci. Remote. Sens., 2022)

Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data. - Wai Tong Chung; Bassem Akoush; Pushan Sharma; Alex Tamkin; Ki Sung Jung; Jacqueline Chen; Jack Guo; Davy Brouzet; Mohsen Talei; Bruno Savard; Alexei Y. Poludnenko; Matthias Ihme (NeurIPS, 2023)

Accelerating large-eddy simulations of clouds with Tensor Processing Units. - Sheide Chammas; Qing Wang 032; Tapio Schneider; Matthias Ihme; Yi-Fan Chen; John R. Anderson (CoRR, 2023)

A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport. - M. Giselle Fernández-Godino; Wai Tong Chung; Akshay A. Gowardhan; Matthias Ihme; Qingkai Kong; Donald D. Lucas; Stephen C. Myers (CoRR, 2024)

Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview. - Andre Nicolle; Sili Deng; Matthias Ihme; Nursulu Kuzhagaliyeva; Emad Al Ibrahim; Aamir Farooq (J. Chem. Inf. Model., 2024)

A regularized-interface method as a unified formulation for simulations of high-pressure multiphase flows. - Nguyen Ly; Matthias Ihme (J. Comput. Phys., 2024)

A high-order diffused-interface approach for two-phase compressible flow simulations using a discontinuous Galerkin framework. - Niccolò Tonicello; Matthias Ihme (J. Comput. Phys., 2024)

Approximately pressure-equilibrium-preserving scheme for fully conservative simulations of compressible multi-species and real-fluid interfacial flows. - H. Terashima; Nguyen Ly; Matthias Ihme (J. Comput. Phys., 2025)