1. Rezaian, E., Huang, C., and Duraisamy, K., Non-intrusive Balancing Transformation of Highly Stiff Systems with Lightly-damped Impulse Response, arXiv preprint arxiv:2109.10408, 2021.

  2. Jacobsen, C. and Duraisamy, K., Disentangling Generative Factors of Physical Fields Using Variational Autoencoders, arXiv preprint arxiv:2109.09510, 2021.

  3. Xu, J., Pradhan, A., and Duraisamy, K., Conditionally Parameterized, Discretization-aware Neural Networks for Mesh-based Modeling of Physical Systems, Accepted to NeurIPS.

  4. Ghattas, O. and Willcox, K., Learning physics-based models from data: Perspectives from inverse problems and model reduction, Acta Numerica, Vol. 30, pp. 445-554, 2021.

  5. Chaudhuri, A., Kramer, B., Norton, M., Royset, J., and Willcox, K. Certifiable Risk-Based Engineering Design Optimization, AIAA Journal, to appear.

  6. Kramer, B. and Willcox, K. Balanced Truncation Model Reduction for Lifted Nonlinear Systems, Realization and Model Reduction of Dynamical Systems, to appear.

  7. Qian, E., Farcas, I.G., and Willcox, K. Reduced operator inference for nonlinear partial differential equations, arXiv preprint arXiv:2102.00083, 2021.

  8. McQuarrie, S.A., Huang, C., and Willcox, K.E. Data-driven reduced-order models via regularised operator inference for a single-injector combustion process, Journal of the Royal Society of New Zealand, Vol. 51, No. 2, pp. 194-211, 2021.

  9. Wentland, C.R., Huang, C., and Duraisamy, K. Investigation of sampling strategies for reduced-order models of rocket combustors, AIAA Scitech Forum, 2021.

  10. Benner, P., Goyal, P., Kramer, B., Peherstorfer, B., and Willcox, K. Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear terms, CMAME, Vol. 372, 2020.

  11. Rim, D., Venturi, L., Bruna, J., and Peherstorfer, B. Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems, arXiv preprint arXiv:2007.13977, 2020.

  12. Huang, C., Wentland, C.R., Duraisamy, K., Merkle, C. Model Reduction for Multi-Scale Transport Problems using Model-form Preserving Least-Squares Projections with Variable Transformation, Accepted to the Journal of Computational Physics.

  13. Kasthuri, P., Pavithran, I., Krishnan, A., Pawar, S., Sujith, R., Gejji, R., Anderson, W., Marwan, N., and Kurths, J. Recurrence analysis of slow-fast systems, Chaos, Vol. 30, Issue 6, 2020.

  14. Tamanampudi, G., Sardeshmukh, S., Anderson, W. and Huang, C. Combustion instability modeling using multi-mode flame transfer functions and a nonlinear Euler solver, International Journal of Spray and Combustion Dynamics, 2020.

  15. Pons, A., Sardeshmukh, S. and Anderson, W. Numerical solution of the pressure response to an unsteady heat release pulse in 1D, Combustion and Flame, 2020.

  16. Rim, D. Exact and fast inversion of the approximate discrete Radon transform from partial data, Applied Math Letters, Vol. 102, 2020.

  17. Pan, S., Duraisamy, K., On the Structure of Time-delay Embedding in Linear Models of Non-linear Dynamical Systems, Chaos, Vol. 30, Issue 7, 2020.

  18. Xu, J. and Duraisamy, K., Multi-level Convolutional Autoencoder Networks for Parametric Prediction of Spatio-temporal Dynamics, CMAME, Vol. 372, 2020.

  19. Qian, E., Kramer, B., Peherstorfer, B., and Willcox, K., Lift & Learn: Physics-informed machine learning for large-scale nonlinear dynamical systems, Physica D, Vol. 406, 2020.

  20. Swischuk, R., Kramer, B., Huang, C., Willcox, K., Learning physics-based reduced-order models for a single-injector combustion process, AIAA Journal, 2020.

  21. Parish, E., Wentland, C.R., and Duraisamy, K., The Adjoint Petrov-Galerkin Method for Non-Linear Model Reduction, CMAME, Vol. 365, 2020.

  22. Kasthuri, P., Pavithran, I., Pawar, S., Sujith, R., Gejji, R., and Anderson, W., Dynamical systems approach to study thermoacoustic transitions in a liquid rocket combustor, Chaos, Vol. 29, Issue 10, 2019.

  23. Rim, D., Peherstorfer, B., and Mandli, K.T. Manifold approximations via transported subspaces: Model reduction for transport-dominated problems, arXiv preprint arXiv:1912.13024, 2019.

  24. Cortinovis, A., Kressner, D., Massei, S., and Peherstorfer, B., Quasi-optimal sampling to learn basis updates for online adaptive model reduction with adaptive empirical interpolation. MATHICSE Technical Report, EPF Lausanne, 2019.

  25. Huang, C., Duraisamy, K., and Merkle, C., Investigations and Improvement of Robustness of Reduced-Order Models of Reacting Flow, AIAA Journal, 2019.

  26. Kramer, B., Marques, A., Peherstorfer, B., Villa, U. and Willcox, K., Multifidelity probability estimation via fusion of estimators, Journal of Computational Physics, Vol. 392, pp. 385-402, 2019.

  27. Qian, E, Kramer, B., Marques, A., and Willcox, K., Transform & Learn: A data-driven approach to nonlinear model reduction. AIAA Aviation Forum, 2019.

  28. Wentland, C.R., Huang, C., and Duraisamy, K., Closure of Reacting Flow Reduced-Order Models via the Adjoint Petrov-Galerkin Method, AIAA Aviation Forum, 2019.

  29. Xu, J., Huang, C., and Duraisamy, K., Reduced-Order Modeling Framework for Combustor Instabilities Using Truncated Domain Training. AIAA Journal, 2019.

  30. Kramer, B., and Willcox, K., Nonlinear model order reduction via lifting transformations and proper orthogonal decomposition, AIAA Journal, 2019.

  31. Marques, A., Lam, R., Chaudhuri, A., Opgenoord, M. and Willcox, K., A multifidelity method for locating aeroelastic flutter boundaries, AIAA SciTech Forum, 2019.

  32. Chaudhuri, A., Kramer, B., and Willcox, K., Information Reuse for Importance Sampling in Reliability-Based Design Optimization, Reliability Engineering & System Safety, Vol. 201, 2020.

  33. Swischuk, R., Mainini, L., Peherstorfer, B., and Willcox, K. Projection-based model reduction: Formulations for physics-based machine learning, Computers and Fluids, Vol. 179, pp. 704-717, 2019.

  34. Peherstorfer, B., Model reduction for transport-dominated problems via online adaptive bases and adaptive sampling, SIAM Journal on Scientific Computing, Vol. 45, No. 5, pp. A2803-2836, 2020.

  35. Marques, A., Lam, R. and Willcox, K., Contour location via entropy reduction leveraging multiple information sources, Advances in Neural Information Processing Systems 31 (NeurIPS), 2018.

  36. Pan, S., and Duraisamy, K., Long-time predictive modeling of nonlinear dynamical systems using neural networks, Complexity, Vol. 2018, 2018.

  37. Pan, S., and Duraisamy, K., Data-driven Discovery of Closure Models, SIAM Journal of Applied Dynamical Systems, Vol. 17, No. 4, pp. 2381-2413, 2018.

  38. Peherstorfer, B., Drmac, Z., and Gugercin, S., Stability of discrete empirical interpolation and gappy proper orthogonal decomposition with randomized and deterministic sampling points, SIAM Journal on Scientific Computing, Vol. 42, No. 5, pp.A2837-A2864, 2020.

  39. Huang, C., K. Duraisamy, and C. Merkle, Challenges in Reduced Order Modeling of Reacting Flows, AIAA Joint Propulsion Conference, 2018.

  40. Tamananpudi, G., Sardeshmukh, S., and Anderson, W., Sensitivity Analysis of Implemented CFD-based Flame Transfer Functions in a Non-linear Euler Solver, AIAA Joint Propulsion Conference, 2018.

  41. Xu, J., Huang, C., and Duraisamy, K., Multi-Domain Reduced-Order Modeling with Sparse Acceleration of Combustion Instability, AIAA Joint Propulsion Conference, 2018.

  42. Peherstorfer, B., Kramer, B. and Willcox, K., Multi-fidelity preconditioning of the cross-entropy method for rare event simulation and failure probability estimation, SIAM/ASA Journal on Uncertainty Quantification, Vol. 6, No. 2, pp. 737-761, 2018.

  43. Huang, C., J. Xu, K. Duraisamy, and C. Merkle. Exploration of Reduced-Order Models for Rocket Combustion Applications, AIAA Aerospace Sciences Meeting, 2018.

  44. Xu, J. and Duraisamy, K. Reduced-Order Modeling of Model Rocket Combustors, AIAA Joint Propulsion Conference, 2017.