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ORCIDMatthias Ihme
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2020 – today
- 2024
[j18]Andre Nicolle
, Sili Deng, Matthias Ihme, Nursulu Kuzhagaliyeva, Emad Al Ibrahim, Aamir Farooq:
Mixtures Recomposition by Neural Nets: A Multidisciplinary Overview. J. Chem. Inf. Model. 64(3): 597-620 (2024)- [j17]Niccolò Tonicello, Matthias Ihme:
A high-order diffused-interface approach for two-phase compressible flow simulations using a discontinuous Galerkin framework. J. Comput. Phys. 508: 112983 (2024) - [j16]Nguyen Ly, Matthias Ihme:
A regularized-interface method as a unified formulation for simulations of high-pressure multiphase flows. J. Comput. Phys. 518: 113310 (2024) - 2023
- [c2]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:
Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data. NeurIPS 2023 - [i10]Sheide Chammas, Qing Wang, Tapio Schneider, Matthias Ihme, Yi-Fan Chen, John R. Anderson:
Accelerating large-eddy simulations of clouds with Tensor Processing Units. CoRR abs/2301.04698 (2023) - [i9]Wai Tong Chung, Bassem Akoush, Pushan Sharma, Alex Tamkin, Ki Sung Jung, Jacqueline H. Chen, Jack Guo, Davy Brouzet, Mohsen Talei, Bruno Savard, Alexei Y. Poludnenko, Matthias Ihme:
Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data. CoRR abs/2309.13457 (2023) - 2022
- [j15]Qing Wang, Matthias Ihme, Yi-Fan Chen, John R. Anderson:
A TensorFlow simulation framework for scientific computing of fluid flows on tensor processing units. Comput. Phys. Commun. 274: 108292 (2022) - [j14]Eric J. Ching, Brett Bornhoft, Ali Lasemi, Matthias Ihme:
Quail: A lightweight open-source discontinuous Galerkin code in Python for teaching and prototyping. SoftwareX 17: 100982 (2022) - [j13]Emeric Boigné, Dilworth Y. Parkinson, Matthias Ihme:
Towards Data-Informed Motion Artifact Reduction in Quantitative CT Using Piecewise Linear Interpolation. IEEE Trans. Computational Imaging 8: 917-932 (2022) - [j12]Fantine Huot, R. Lily Hu, Nita Goyal, Tharun Sankar, Matthias Ihme, Yi-Fan Chen:
Next Day Wildfire Spread: A Machine Learning Dataset to Predict Wildfire Spreading From Remote-Sensing Data. IEEE Trans. Geosci. Remote. Sens. 60: 1-13 (2022) - [i8]Wai Tong Chung, Ki Sung Jung, Jacqueline H. Chen, Matthias Ihme:
The Bearable Lightness of Big Data: Towards Massive Public Datasets in Scientific Machine Learning. CoRR abs/2207.12546 (2022) - [i7]John Burge, Matthew R. Bonanni, R. Lily Hu, Matthias Ihme:
Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior. CoRR abs/2210.16411 (2022) - 2021
- [j11]Eric J. Ching, Matthias Ihme:
Efficient projection kernels for discontinuous Galerkin simulations of disperse multiphase flows on arbitrary curved elements. J. Comput. Phys. 435: 110266 (2021) - [j10]Eric J. Ching, Matthias Ihme:
Development of a particle collision algorithm for discontinuous Galerkin simulations of compressible multiphase flows. J. Comput. Phys. 436: 110319 (2021) - [c1]Wai Tong Chung, Aashwin Ananda Mishra, Nikolaos Perakis, Matthias Ihme:
Accelerating High-fidelity Combustion Simulations with Classification Algorithms. AAAI Spring Symposium: MLPS 2021 - [i6]Wai Tong Chung, Aashwin Ananda Mishra, Matthias Ihme:
Interpretable Data-driven Methods for Subgrid-scale Closure in LES for Transcritical LOX/GCH4 Combustion. CoRR abs/2103.06397 (2021) - [i5]Qing Wang, Matthias Ihme, Yi-Fan Chen, John R. Anderson:
A TensorFlow Simulation Framework for Scientific Computing of Fluid Flows on Tensor Processing Units. CoRR abs/2108.11076 (2021) - [i4]Fantine Huot, R. Lily Hu, Nita Goyal, Tharun Sankar, Matthias Ihme, Yi-Fan Chen:
Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data. CoRR abs/2112.02447 (2021) - 2020
- [j9]Eric J. Ching, Steven R. Brill, Michael Barnhardt, Matthias Ihme:
A two-way coupled Euler-Lagrange method for simulating multiphase flows with discontinuous Galerkin schemes on arbitrary curved elements. J. Comput. Phys. 405: 109096 (2020) - [i3]Wai Tong Chung, Aashwin Ananda Mishra, Nikolaos Perakis, Matthias Ihme:
Data-assisted combustion simulations with dynamic submodel assignment using random forests. CoRR abs/2009.04023 (2020) - [i2]Fantine Huot, R. Lily Hu, Matthias Ihme, Qing Wang, John Burge, Tianjian Lu, Jason Hickey, Yi-Fan Chen, John R. Anderson:
Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data. CoRR abs/2010.07445 (2020) - [i1]John Burge, Matthew Bonanni, Matthias Ihme, R. Lily Hu:
Convolutional LSTM Neural Networks for Modeling Wildland Fire Dynamics. CoRR abs/2012.06679 (2020)
2010 – 2019
- 2019
- [j8]Hao Wu, Peter C. Ma, Matthias Ihme:
Efficient time-stepping techniques for simulating turbulent reactive flows with stiff chemistry. Comput. Phys. Commun. 243: 81-96 (2019) - [j7]Eric J. Ching, Yu Lv, Peter A. Gnoffo, Michael Barnhardt, Matthias Ihme:
Shock capturing for discontinuous Galerkin methods with application to predicting heat transfer in hypersonic flows. J. Comput. Phys. 376: 54-75 (2019) - 2017
- [j6]Peter C. Ma, Yu Lv, Matthias Ihme:
An entropy-stable hybrid scheme for simulations of transcritical real-fluid flows. J. Comput. Phys. 340: 330-357 (2017) - 2016
- [j5]Yu Lv, Yee Chee See, Matthias Ihme:
An entropy-residual shock detector for solving conservation laws using high-order discontinuous Galerkin methods. J. Comput. Phys. 322: 448-472 (2016) - 2015
- [j4]Yu Lv, Matthias Ihme:
Entropy-bounded discontinuous Galerkin scheme for Euler equations. J. Comput. Phys. 295: 715-739 (2015) - 2014
- [j3]Yu Lv, Matthias Ihme:
Discontinuous Galerkin method for multicomponent chemically reacting flows and combustion. J. Comput. Phys. 270: 105-137 (2014) - 2012
- [j2]Matthias Ihme, Lee Shunn, Jian Zhang:
Regularization of reaction progress variable for application to flamelet-based combustion models. J. Comput. Phys. 231(23): 7715-7721 (2012)
2000 – 2009
- 2008
- [j1]Matthias Ihme, Alison L. Marsden, Heinz Pitsch:
Generation of Optimal Artificial Neural Networks Using a Pattern Search Algorithm: Application to Approximation of Chemical Systems. Neural Comput. 20(2): 573-601 (2008)
Coauthor Index
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