Publications
Many of the following are available for online reading at arXiv.
**Pre-prints
arXiv:2102.04393: Yang Zheng, Yujie Tang, Na Li, “Analysis of the Optimization Landscape of Linear Quadratic Gaussian (LQG) Control”, Mathematical Programming, Accepted
arXiv:2210.04810: Bin Hu, Kaiqing Zhang, Na Li, Mehran Mesbahi, Maryam Fazel, Tamer Basar, “Towards a Theoretical Foundation of Policy Optimization for Learning Control Policies”, Annual Review of Control, Robotics, and Autonomous Systems, accepted
Zhaolin Ren, Yujie Tang, Na Li, “Escaping saddle points in zeroth-order optimization: two function evaluations suffice”, ICML, 2023 (submitted)
Yingying Li, James A Preiss, Na Li, Yiheng Lin, Adam Wierman, Jeff S Shamma, “Online switching control with stability and regret guarantees”, L4DC, 2023
arXiv:2212.08765: Tongzheng Ren, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, Bo Dai, “Latent Variable Representation for Reinforcement Learning”, ICLR 2023
arXiv:2211.07767: Hanjun Dai, Yuan Xue, Niao He, Bethany Wang, Na Li, Dale Schuurmans, Bo Dai, “Learning to Optimize for Stochastic Dominance Constraints”, AISTATS 2023
*Yingying Li, Tianpeng Zhang, Subhro Das, Jeff Shamma, Na Li, “Non-asymptotic System Identification for Linear Systems with Nonline
ar Policies”, IFAC 2023
arXiv:2111.00411: Yingying Li, Subhro Das, Jeff Shamma, Na Li, “Safe Adaptive Learning-based Control for Constrained Linear Quadratic Regulators with Regret Guarantees.”
Runyu Zhang, Weiyu Li, Na Li, “On the Optimal Control of Network LQR with Spatially-Exponential Decaying Structure”, ACC 2023
Yujie Tang, Zhaolin Ren, Na Li, “Zeroth-Order Feedback Optimization for Cooperative Multi-Agent Systems”, Automatica 148: 110741, 2023
arXiv:2209.10132: K. Kaheman, J. Bramburger, J. N. Kutz, and S. L. Brunton. Saddle
Transport and Chaos in the Double Pendulum. To appear in Nonlinear Dynamics. (2023).
arXiv:2111.11299: M. Hickner, U. Fasel, A. G. Nair, B. W. Brunton, S. L. Brunton. Data-driven aeroelastic modeling for control. AIAA Journal, 61(2):780–792, (2023).
arXiv:2204.03216: S. Pan, S. L. Brunton, and J. N. Kutz. Neural Implicit Flow: a mesh-agnostic dimension reduction paradigm of spatio-temporal data. Journal of Machine Learning Research, 24(41):1–60, (2023).
**arXiv:2303.17078: S. L. Brunton and J. N. Kutz. Machine Learning for Partial Differential Equations, (2023).
**arXiv:2304.03326: K. Krishna, S. L. Brunton, and Z. Song. Finite Time Lyapunov Exponent Analysis of Model Predictive Control and Reinforcement Learning, (2023).
**arXiv:2302.10787: A. A. Kaptanoglu, L. Zhang, Z. G. Nicolaou, U. Fasel, and S. L. Brunton. Benchmarking sparse system identification with low-dimensional chaos, (2023).
**arXiv:2301.13093: B. Herrmann, P. J. Baddoo, S. Dawson, R. Semaan, S. L. Brunton, B. J. McKeon. From resolvent to Gramians: extracting force and response modes for control, (2023).
**arXiv:2301.12649: Gao, U. Fasel, S. L. Brunton, and J. N. Kutz. Convergence of uncertainty estimates in ensemble and Bayesian sparse model discovery, (2023).
N. Arya & A. G. Nair, Cluster-based predictive model for nonlinear dynamics. DisCoVor, Colorado, (2023).
**Griffin Smith, Nathan Stouffer, Scott G. McCalla, and Dominique P. Zosso. A PDE Model for Janus Particle Swarming. AMS Contributed Paper Session on Applications of Mathematics.
Houle, J. and van Breugel, F. Near-surface wind variability over spatiotemporal scales relevant to plume tracking insects. (2023). Physics of Fluids (in press).
Singh, S., van Breugel, F., Rao, P. N., and Brunton, B. W. Emergent behaviour and neural dynamics in artificial agents tracking odour plumes. (2023). Nature Machine Intelligence.
**arXiv:2304.14313. Cellini, B., Boyacioglu, B., and van Breugel, F. Empirical Individual State Observability. (2023). (in review at Cold Regions Science and Technology)
**arXiv: 2304.14307: Mclelland, F. and van Breugel, F. A Method for Classifying Snow Using Ski-Mounted Strain Sensors. (2023) (in review for the Control and Decisions Conference)
**Nag, A. and van Breugel, F. Outdoor odor plume measurements reveal statistics correlated with source distance at large spatial scales. (2023).
**Stupski, S. D. and van Breugel, F. Wind gates distinct olfaction driven search states in free-flying Drosophila melanogaster. (2023). (See link for abstract.)
**arXiv:2303.08323: Seyyed A. Fatemi and June Zhang, Estimating Parameters of Large CTMP from Single Trajectory with Application to Stochastic Network Epidemics Models, submitted to the IEEE Transactions on Network Science and Engineering.
Oram J. K., Banner, K. M., Stratton, C., Irvine, K.M. Verifying species classification labels using stratified-by-species sampling reduces cost of long-term acoustic monitoring. Methods in Ecology and Evolution. Awaiting Editor Decision.
**arXiv:2301.05365: Y. Nair and L. Janson. Randomization Tests for Adaptively Collected Data. (2023).
**N. Karnik, C. E. Estrada Perez, J. S. Yoo, J. J. Cogliati, R. S. Skifton, P. Calderoni, S. L. Brunton, M. G. Abdo, and K. Manohar, Optimal Sensor Placement with Adaptive Constraints for Nuclear Digital Twins, Submitted for external release at Idaho National Lab, (2023).
**B. Karlik & A. G. Nair, ``distribute: Easy to use Resource Manager for Distributed Computing without Assumptions, in review, Journal of Open Source Software, (2023). (Abstract N/A.)
**arXiv:2301.01314: A. G. Nair & S. Douglass, ``Network-theoretic modeling of fluid-structure interactions, in review, Theor. Comput. Fluid Dyn., (2023).
Kimchi, O., King, E. M., & Brenner, M. P. (2023). Uncovering the mechanism for aggregation in repeat expanded RNA reveals a reentrant transition. Nature Communications, 14(1), 332.
Zhu F., Jing D., Leve F., and Ferrari S. NN-Poly: Approximating Neural Networks by Taylor Polynomials for Safer State Prediction. Frontiers in Robotics and AI (2022): 23
J. Munson, B. Cummins, D. Zosso, An Introduction to Collaborative Filtering with an Eye Towards Lessons from the Netflix Prize, review article in preparation for ``ACM'').
J. Munson, D. Zosso, Collaborative Filtering for Pandemic-era Grade Dropout Prediction, (in preparation for ``IEEE Transactions on Learning Technologies'').
A. Emmons, H. Fessler, R. Grady, D. Zosso, From Convex Geometry to Optimization without Calculus, (in preparation for ``American Mathematical Monthly'').
C.Potts, A. Kunze, D. Zosso}, Elucidating Nanomaterial-Cell Interaction by Archetypal Analysis, (in preparation for ``Journal of Neuroscience Methods'').
D. Zosso, Graph-based Geometric Data Analysis: Studying the Shape of Data using Integral Geometry for Convex Bodies, (in preparation for ``SIAM Journal on Applied Algebra and Geometry'')
D. Zosso, A. Bustin, N. Sochen, The Beltrami Framework for Variational Image Processing, (review article, in preparation).
G. Smith, N. Stouffer, S. McCalla, D. Zosso, A PDE Model for Janus-Particle Swarming, Physical Review E (submitted 05/(2022), currently in revision).
**arXiv:2212.08280: F. Mei, S. L. Brunton, and J. N. Kutz. Mobile sensor path planning for Kalman filter spatiotemporal estimation, 2022.
**arXiv:2301.02673: Z. G. Nicolaou, G. Huo, Y. Chen, S. L. Brunton, and J. N. Kutz. Data-driven discovery and extrapolation of parameterized pattern-forming dynamics, (2022).
**arXiv:2206.13205: J. L. Callaham, J.-Ch. Loiseau, and S. L. Brunton. Multiscale model reduction for incompressible flows, (2022).
**arXiv:2205.06231: K. Kaheman, U. Fasel, J. Bramburger, B. Strom, J. N. Kutz, and S. L. Brunton. The Experimental Multi-Arm Pendulum on a Cart: A Benchmark System for Chaos, Learning, and Control, (2022).
**)arXiv:2212.05591: Liu, Yuxuan, Scott G. McCalla, and Hayden Schaeffer. Random Feature Models for Learning Interacting Dynamical Systems. arXiv preprint. (2022).
C. Barbour, M. Greenwood, D. Zosso, B. Bielekova, Constructed Composite Response: A framework for constructing targeted latent variables, Statistics in Medicine (submitted 07/(2022).
King, E. M., Wang, Z., Weitz, D. A., Spaepen, F., & Brenner, M. P. (2022). Correlation Tracking: Using simulations to interpolate highly correlated particle tracks. Physical Review E, 105(4), 044608.
Kimchi, Ofer, Michael P. Brenner, and Lucy J. Colwell. "Nucleic Acid Structure Prediction Including Pseudoknots Through Direct Enumeration of States: A User’s Guide to the LandscapeFold Algorithm." RNA Structure Prediction. New York, NY: Springer US, 2022. 49-77.
Du, C. X., Zhang, H. A., Pearson, T. G., Ng, J., McEuen, P. L., Cohen, I., & Brenner, M. P. (2022). Programming interactions in magnetic handshake materials. Soft Matter, 18(34), 6404-6410.
**arXiv:2201.00098: Engel, Megan C., Jamie A. Smith, and Michael P. Brenner. "Optimal control of nonequilibrium systems through automatic differentiation." arXiv preprint (2022)
**Curatolo, A. I., Kimchi, O., Goodrich, C. P., & Brenner, M. P. (2022). The assembly yield of complex, heterogeneous structures: A computational toolbox. bioRxiv, 2022-06.
**arXiv:2212.11886: Page, J., Norgaard, P., Brenner, M. P., & Kerswell, R. R. (2022). Recurrent flow patterns as a basis for turbulence: predicting statistics from structures. arXiv preprint. Shrinivas, K., & Brenner, M. P. (2022). Multiphase coexistence capacity in complex fluids. bioRxiv, 2022-10
arXiv:2110.15013: M. Hoffmann, M. Scherer, T. Hempel, A. Mardt, B. de Silva, B. E. Husic, S. Klus, H. Wu, Nathan Kutz, S. L. Brunton, F. Noé. Deeptime: a Python library for machine learning dynamical models from time series data. Machine Learning Science and Technology, 3(1):015009, (2021).
arXiv:2112.10755: Boyuan Chen, Kuang Huang, Sunand Raghupathi, Ishaan Chandratreya, Qiang Du, Hod Lipson. Discovering State Variables Hidden in Experimental Data.
arXiv:2203.14705: Aminur Rahman, J. Nathan Kutz. Walking droplets as a damped-driven system.
arXiv:2203.05164: Megan R. Ebers, Katherine M. Steele, J. Nathan Kutz. Discrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effects.
arXiv:2202.04643: Joseph Bakarji, Jared Callaham, Steven L. Brunton, J. Nathan Kutz. Dimensionally Consistent Learning with Buckingham Pi.
arXiv:2201.05266: Andy J. Goldschmidt, Jonathan L. DuBois, Steven L. Brunton, J. Nathan Kutz. Model predictive control for robust quantum state preparation.
arXiv:2201.05136: Joseph Bakarji, Kathleen Champion, J. Nathan Kutz, Steven L. Brunton. Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders.
arXiv:2112.04307: Peter J. Baddoo, Benjamin Herrmann, Beverley J. McKeon, J. Nathan Kutz, Steven L. Brunton. Physics-informed dynamic mode decomposition (piDMD)
arXiv:2111.10992: Urban Fasel, J. Nathan Kutz, Bingni W. Brunton, Steven L. Brunton. Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control.
G. Smith, N. Stouffer, S. McCalla and D. Zosso. A PDE Model for Janus Particle Swarming (PRE, in revision)
B. Connor Beck, K. Anja and D. Zosso. Archetypal Analysis for Neuronal Clique Detection in Low Rate Calcium Fluorometry (IEEE Engineering in Medicine and Biology Conference).
C. Dudiak, S. McCalla, K. Ostrem, J. Wilking. Analysis of Dynamic Biological Systems Imagery: P. Dendritiformis Boundary Tracking and Optimized Modeling.
J. Oram and K. Banner, K. Irvine, C. Stratton. Reconciliation of the sampling/data collection process and the use of AI in monitoring wildlife.
K. Taira and A. G. Nair. Network-based analysis of fluid flows: Progress and outlook. Progress in Aerospace Sciences, 131, 100823.
S. Douglass and A. G. Nair. Multi-layer network analysis of fluid-structure interactions.
F. van Breugel, J. Jewell, and J. Houle. Active Anemosensing Hypothesis: How Flying Insects Could Estimate Ambient Wind Direction Through Sensory Integration and Active Movement (2022) bioRxiv).
F. Mclelland, R. Tung, and F. van Breugel, F. Ski Sense, a Method for Classifying Snow Being Skied.
F. van Breugel, B. Brunton and J. N. Kutz. A taxonomy of numerical differentiation methods.
S. Shriwastav, G. Snyder and Z. Song. Dynamic compressed sensing of unsteady flows with a mobile robot (submitted to the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (2022).
G. Yu, G. Li, X. Si and Z. Song. A multi-size kernel based adaptive convolutional neural network for bearing fault diagnosis (submitted to Applied Intelligence, (2022).
E. Hansen, S. L. Brunton and Z. Song. Swarm modeling with dynamic mode decomposition (IEEE Access, vol. 10, pp. 59508-59521, (2022).
Massimo Aufiero and Lucas Janson. Surrogate-based global sensitivity analysis with statistical guarantees.
Feicheng Wang and Lucas Janson. Rate-matching the Regret Lower-bound in the Linear Quadratic Regulator With Unknown Dynamics.
Yang Zheng, Yue Sun, Maryam Fazel, Na Li. Escaping High-order Saddles in Policy Optimization for Linear Quadratic Gaussian (LQG) Control.
Sam Buchanan, Jingkai Yan, Ellie Haber, John Wright. Resource-Efficient Invariant Networks: Exponential Gains by Unrolled Optimization.
Jingkai Yan, Robert Colgan, John Wright, Zsuzsa Marka, Imre Bartos, Szabolcs Marka. Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks.
John Whitehead, Hod Lipson. Multi-Process Printing Combining Powder and Resin Based Additive Manufacturing, Additive Manufacturing Letters, (2022).
**Pre-prints
arXiv:2102.04393: Yang Zheng, Yujie Tang, Na Li, “Analysis of the Optimization Landscape of Linear Quadratic Gaussian (LQG) Control”, Mathematical Programming, Accepted
arXiv:2210.04810: Bin Hu, Kaiqing Zhang, Na Li, Mehran Mesbahi, Maryam Fazel, Tamer Basar, “Towards a Theoretical Foundation of Policy Optimization for Learning Control Policies”, Annual Review of Control, Robotics, and Autonomous Systems, accepted
Zhaolin Ren, Yujie Tang, Na Li, “Escaping saddle points in zeroth-order optimization: two function evaluations suffice”, ICML, 2023 (submitted)
Yingying Li, James A Preiss, Na Li, Yiheng Lin, Adam Wierman, Jeff S Shamma, “Online switching control with stability and regret guarantees”, L4DC, 2023
arXiv:2212.08765: Tongzheng Ren, Chenjun Xiao, Tianjun Zhang, Na Li, Zhaoran Wang, Sujay Sanghavi, Dale Schuurmans, Bo Dai, “Latent Variable Representation for Reinforcement Learning”, ICLR 2023
arXiv:2211.07767: Hanjun Dai, Yuan Xue, Niao He, Bethany Wang, Na Li, Dale Schuurmans, Bo Dai, “Learning to Optimize for Stochastic Dominance Constraints”, AISTATS 2023
*Yingying Li, Tianpeng Zhang, Subhro Das, Jeff Shamma, Na Li, “Non-asymptotic System Identification for Linear Systems with Nonline
ar Policies”, IFAC 2023
arXiv:2111.00411: Yingying Li, Subhro Das, Jeff Shamma, Na Li, “Safe Adaptive Learning-based Control for Constrained Linear Quadratic Regulators with Regret Guarantees.”
Runyu Zhang, Weiyu Li, Na Li, “On the Optimal Control of Network LQR with Spatially-Exponential Decaying Structure”, ACC 2023
Yujie Tang, Zhaolin Ren, Na Li, “Zeroth-Order Feedback Optimization for Cooperative Multi-Agent Systems”, Automatica 148: 110741, 2023
arXiv:2209.10132: K. Kaheman, J. Bramburger, J. N. Kutz, and S. L. Brunton. Saddle
Transport and Chaos in the Double Pendulum. To appear in Nonlinear Dynamics. (2023).
arXiv:2111.11299: M. Hickner, U. Fasel, A. G. Nair, B. W. Brunton, S. L. Brunton. Data-driven aeroelastic modeling for control. AIAA Journal, 61(2):780–792, (2023).
arXiv:2204.03216: S. Pan, S. L. Brunton, and J. N. Kutz. Neural Implicit Flow: a mesh-agnostic dimension reduction paradigm of spatio-temporal data. Journal of Machine Learning Research, 24(41):1–60, (2023).
**arXiv:2303.17078: S. L. Brunton and J. N. Kutz. Machine Learning for Partial Differential Equations, (2023).
**arXiv:2304.03326: K. Krishna, S. L. Brunton, and Z. Song. Finite Time Lyapunov Exponent Analysis of Model Predictive Control and Reinforcement Learning, (2023).
**arXiv:2302.10787: A. A. Kaptanoglu, L. Zhang, Z. G. Nicolaou, U. Fasel, and S. L. Brunton. Benchmarking sparse system identification with low-dimensional chaos, (2023).
**arXiv:2301.13093: B. Herrmann, P. J. Baddoo, S. Dawson, R. Semaan, S. L. Brunton, B. J. McKeon. From resolvent to Gramians: extracting force and response modes for control, (2023).
**arXiv:2301.12649: Gao, U. Fasel, S. L. Brunton, and J. N. Kutz. Convergence of uncertainty estimates in ensemble and Bayesian sparse model discovery, (2023).
N. Arya & A. G. Nair, Cluster-based predictive model for nonlinear dynamics. DisCoVor, Colorado, (2023).
**Griffin Smith, Nathan Stouffer, Scott G. McCalla, and Dominique P. Zosso. A PDE Model for Janus Particle Swarming. AMS Contributed Paper Session on Applications of Mathematics.
Houle, J. and van Breugel, F. Near-surface wind variability over spatiotemporal scales relevant to plume tracking insects. (2023). Physics of Fluids (in press).
Singh, S., van Breugel, F., Rao, P. N., and Brunton, B. W. Emergent behaviour and neural dynamics in artificial agents tracking odour plumes. (2023). Nature Machine Intelligence.
**arXiv:2304.14313. Cellini, B., Boyacioglu, B., and van Breugel, F. Empirical Individual State Observability. (2023). (in review at Cold Regions Science and Technology)
**arXiv: 2304.14307: Mclelland, F. and van Breugel, F. A Method for Classifying Snow Using Ski-Mounted Strain Sensors. (2023) (in review for the Control and Decisions Conference)
**Nag, A. and van Breugel, F. Outdoor odor plume measurements reveal statistics correlated with source distance at large spatial scales. (2023).
**Stupski, S. D. and van Breugel, F. Wind gates distinct olfaction driven search states in free-flying Drosophila melanogaster. (2023). (See link for abstract.)
**arXiv:2303.08323: Seyyed A. Fatemi and June Zhang, Estimating Parameters of Large CTMP from Single Trajectory with Application to Stochastic Network Epidemics Models, submitted to the IEEE Transactions on Network Science and Engineering.
Oram J. K., Banner, K. M., Stratton, C., Irvine, K.M. Verifying species classification labels using stratified-by-species sampling reduces cost of long-term acoustic monitoring. Methods in Ecology and Evolution. Awaiting Editor Decision.
**arXiv:2301.05365: Y. Nair and L. Janson. Randomization Tests for Adaptively Collected Data. (2023).
**N. Karnik, C. E. Estrada Perez, J. S. Yoo, J. J. Cogliati, R. S. Skifton, P. Calderoni, S. L. Brunton, M. G. Abdo, and K. Manohar, Optimal Sensor Placement with Adaptive Constraints for Nuclear Digital Twins, Submitted for external release at Idaho National Lab, (2023).
**B. Karlik & A. G. Nair, ``distribute: Easy to use Resource Manager for Distributed Computing without Assumptions, in review, Journal of Open Source Software, (2023). (Abstract N/A.)
**arXiv:2301.01314: A. G. Nair & S. Douglass, ``Network-theoretic modeling of fluid-structure interactions, in review, Theor. Comput. Fluid Dyn., (2023).
Kimchi, O., King, E. M., & Brenner, M. P. (2023). Uncovering the mechanism for aggregation in repeat expanded RNA reveals a reentrant transition. Nature Communications, 14(1), 332.
Zhu F., Jing D., Leve F., and Ferrari S. NN-Poly: Approximating Neural Networks by Taylor Polynomials for Safer State Prediction. Frontiers in Robotics and AI (2022): 23
J. Munson, B. Cummins, D. Zosso, An Introduction to Collaborative Filtering with an Eye Towards Lessons from the Netflix Prize, review article in preparation for ``ACM'').
J. Munson, D. Zosso, Collaborative Filtering for Pandemic-era Grade Dropout Prediction, (in preparation for ``IEEE Transactions on Learning Technologies'').
A. Emmons, H. Fessler, R. Grady, D. Zosso, From Convex Geometry to Optimization without Calculus, (in preparation for ``American Mathematical Monthly'').
C.Potts, A. Kunze, D. Zosso}, Elucidating Nanomaterial-Cell Interaction by Archetypal Analysis, (in preparation for ``Journal of Neuroscience Methods'').
D. Zosso, Graph-based Geometric Data Analysis: Studying the Shape of Data using Integral Geometry for Convex Bodies, (in preparation for ``SIAM Journal on Applied Algebra and Geometry'')
D. Zosso, A. Bustin, N. Sochen, The Beltrami Framework for Variational Image Processing, (review article, in preparation).
G. Smith, N. Stouffer, S. McCalla, D. Zosso, A PDE Model for Janus-Particle Swarming, Physical Review E (submitted 05/(2022), currently in revision).
**arXiv:2212.08280: F. Mei, S. L. Brunton, and J. N. Kutz. Mobile sensor path planning for Kalman filter spatiotemporal estimation, 2022.
**arXiv:2301.02673: Z. G. Nicolaou, G. Huo, Y. Chen, S. L. Brunton, and J. N. Kutz. Data-driven discovery and extrapolation of parameterized pattern-forming dynamics, (2022).
**arXiv:2206.13205: J. L. Callaham, J.-Ch. Loiseau, and S. L. Brunton. Multiscale model reduction for incompressible flows, (2022).
**arXiv:2205.06231: K. Kaheman, U. Fasel, J. Bramburger, B. Strom, J. N. Kutz, and S. L. Brunton. The Experimental Multi-Arm Pendulum on a Cart: A Benchmark System for Chaos, Learning, and Control, (2022).
**)arXiv:2212.05591: Liu, Yuxuan, Scott G. McCalla, and Hayden Schaeffer. Random Feature Models for Learning Interacting Dynamical Systems. arXiv preprint. (2022).
C. Barbour, M. Greenwood, D. Zosso, B. Bielekova, Constructed Composite Response: A framework for constructing targeted latent variables, Statistics in Medicine (submitted 07/(2022).
King, E. M., Wang, Z., Weitz, D. A., Spaepen, F., & Brenner, M. P. (2022). Correlation Tracking: Using simulations to interpolate highly correlated particle tracks. Physical Review E, 105(4), 044608.
Kimchi, Ofer, Michael P. Brenner, and Lucy J. Colwell. "Nucleic Acid Structure Prediction Including Pseudoknots Through Direct Enumeration of States: A User’s Guide to the LandscapeFold Algorithm." RNA Structure Prediction. New York, NY: Springer US, 2022. 49-77.
Du, C. X., Zhang, H. A., Pearson, T. G., Ng, J., McEuen, P. L., Cohen, I., & Brenner, M. P. (2022). Programming interactions in magnetic handshake materials. Soft Matter, 18(34), 6404-6410.
**arXiv:2201.00098: Engel, Megan C., Jamie A. Smith, and Michael P. Brenner. "Optimal control of nonequilibrium systems through automatic differentiation." arXiv preprint (2022)
**Curatolo, A. I., Kimchi, O., Goodrich, C. P., & Brenner, M. P. (2022). The assembly yield of complex, heterogeneous structures: A computational toolbox. bioRxiv, 2022-06.
**arXiv:2212.11886: Page, J., Norgaard, P., Brenner, M. P., & Kerswell, R. R. (2022). Recurrent flow patterns as a basis for turbulence: predicting statistics from structures. arXiv preprint. Shrinivas, K., & Brenner, M. P. (2022). Multiphase coexistence capacity in complex fluids. bioRxiv, 2022-10
arXiv:2110.15013: M. Hoffmann, M. Scherer, T. Hempel, A. Mardt, B. de Silva, B. E. Husic, S. Klus, H. Wu, Nathan Kutz, S. L. Brunton, F. Noé. Deeptime: a Python library for machine learning dynamical models from time series data. Machine Learning Science and Technology, 3(1):015009, (2021).
arXiv:2112.10755: Boyuan Chen, Kuang Huang, Sunand Raghupathi, Ishaan Chandratreya, Qiang Du, Hod Lipson. Discovering State Variables Hidden in Experimental Data.
arXiv:2203.14705: Aminur Rahman, J. Nathan Kutz. Walking droplets as a damped-driven system.
arXiv:2203.05164: Megan R. Ebers, Katherine M. Steele, J. Nathan Kutz. Discrepancy Modeling Framework: Learning missing physics, modeling systematic residuals, and disambiguating between deterministic and random effects.
arXiv:2202.04643: Joseph Bakarji, Jared Callaham, Steven L. Brunton, J. Nathan Kutz. Dimensionally Consistent Learning with Buckingham Pi.
arXiv:2201.05266: Andy J. Goldschmidt, Jonathan L. DuBois, Steven L. Brunton, J. Nathan Kutz. Model predictive control for robust quantum state preparation.
arXiv:2201.05136: Joseph Bakarji, Kathleen Champion, J. Nathan Kutz, Steven L. Brunton. Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders.
arXiv:2112.04307: Peter J. Baddoo, Benjamin Herrmann, Beverley J. McKeon, J. Nathan Kutz, Steven L. Brunton. Physics-informed dynamic mode decomposition (piDMD)
arXiv:2111.10992: Urban Fasel, J. Nathan Kutz, Bingni W. Brunton, Steven L. Brunton. Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control.
G. Smith, N. Stouffer, S. McCalla and D. Zosso. A PDE Model for Janus Particle Swarming (PRE, in revision)
B. Connor Beck, K. Anja and D. Zosso. Archetypal Analysis for Neuronal Clique Detection in Low Rate Calcium Fluorometry (IEEE Engineering in Medicine and Biology Conference).
C. Dudiak, S. McCalla, K. Ostrem, J. Wilking. Analysis of Dynamic Biological Systems Imagery: P. Dendritiformis Boundary Tracking and Optimized Modeling.
J. Oram and K. Banner, K. Irvine, C. Stratton. Reconciliation of the sampling/data collection process and the use of AI in monitoring wildlife.
K. Taira and A. G. Nair. Network-based analysis of fluid flows: Progress and outlook. Progress in Aerospace Sciences, 131, 100823.
S. Douglass and A. G. Nair. Multi-layer network analysis of fluid-structure interactions.
F. van Breugel, J. Jewell, and J. Houle. Active Anemosensing Hypothesis: How Flying Insects Could Estimate Ambient Wind Direction Through Sensory Integration and Active Movement (2022) bioRxiv).
F. Mclelland, R. Tung, and F. van Breugel, F. Ski Sense, a Method for Classifying Snow Being Skied.
F. van Breugel, B. Brunton and J. N. Kutz. A taxonomy of numerical differentiation methods.
S. Shriwastav, G. Snyder and Z. Song. Dynamic compressed sensing of unsteady flows with a mobile robot (submitted to the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (2022).
G. Yu, G. Li, X. Si and Z. Song. A multi-size kernel based adaptive convolutional neural network for bearing fault diagnosis (submitted to Applied Intelligence, (2022).
E. Hansen, S. L. Brunton and Z. Song. Swarm modeling with dynamic mode decomposition (IEEE Access, vol. 10, pp. 59508-59521, (2022).
Massimo Aufiero and Lucas Janson. Surrogate-based global sensitivity analysis with statistical guarantees.
Feicheng Wang and Lucas Janson. Rate-matching the Regret Lower-bound in the Linear Quadratic Regulator With Unknown Dynamics.
Yang Zheng, Yue Sun, Maryam Fazel, Na Li. Escaping High-order Saddles in Policy Optimization for Linear Quadratic Gaussian (LQG) Control.
Sam Buchanan, Jingkai Yan, Ellie Haber, John Wright. Resource-Efficient Invariant Networks: Exponential Gains by Unrolled Optimization.
Jingkai Yan, Robert Colgan, John Wright, Zsuzsa Marka, Imre Bartos, Szabolcs Marka. Boosting the Efficiency of Parametric Detection with Hierarchical Neural Networks.
John Whitehead, Hod Lipson. Multi-Process Printing Combining Powder and Resin Based Additive Manufacturing, Additive Manufacturing Letters, (2022).