Library · 17-control-engineering
Control & SciML
| Title | Peer | Link |
|---|---|---|
| A New Approach to Linear Filtering and Prediction Problems (Kalman filter) | ✓ peer | doi.org/10.1115/1.3662552 |
| Differentiable MPC for End-to-end Planning and Control | ◦ preprint | arxiv.org/abs/1810.13400 |
| End-to-End Training of Deep Visuomotor Policies (Guided Policy Search) | ◦ preprint | arxiv.org/abs/1504.00702 |
| Deep learning for universal linear embeddings of nonlinear dynamics (Koopman) | ◦ preprint | arxiv.org/abs/1712.09707 |
| Control Barrier Functions: Theory and Applications | — unref | arxiv.org/abs/1903.11199 |
| Data-Enabled Predictive Control: In the Shallows of the DeePC | — unref | arxiv.org/abs/1811.05890 |
| Neural Lyapunov Control | ◦ preprint | arxiv.org/abs/2005.00611 |
| Gaussian Processes for Data-Efficient Learning in Robotics and Control (PILCO) | ◦ preprint | arxiv.org/abs/1502.02860 |
| Learning-Based Model Predictive Control: Toward Safe Learning in Control | ✓ peer | doi.org/10.1146/annurev-control-090419-075625 |
| Fuzzy Identification of Systems and Its Applications to Modeling and Control (Takagi–Sugeno) | ✓ peer | doi.org/10.1109/TSMC.1985.6313399 |
| A Tour of Reinforcement Learning: The View from Continuous Control | ◦ preprint | arxiv.org/abs/1806.09460 |
| Sparse Identification of Nonlinear Dynamics (SINDy) | ◦ preprint | arxiv.org/abs/1509.03580 |
| Neural Ordinary Differential Equations | ◦ preprint | arxiv.org/abs/1806.07366 |
| Augmented Neural ODEs | ◦ preprint | arxiv.org/abs/1904.01681 |
| ODE²VAE: Deep Generative Second Order ODEs with Bayesian NNs | ◦ preprint | arxiv.org/abs/1905.10994 |
| Stiff Neural Ordinary Differential Equations | ◦ preprint | arxiv.org/abs/2103.15341 |
| Deep Kalman Filters | ◦ preprint | arxiv.org/abs/1511.05121 |
| Structured Inference Networks for Nonlinear State Space Models | — unref | arxiv.org/abs/1609.09869 |
| KalmanNet: NN-Aided Kalman Filtering for Partially Known Dynamics | ◦ preprint | arxiv.org/abs/2107.10043 |
| Backprop KF: Learning Discriminative Deterministic State Estimators | ◦ preprint | arxiv.org/abs/1605.07148 |
| Latent ODEs for Irregularly-Sampled Time Series | ◦ preprint | arxiv.org/abs/1907.03907 |
| Scalable Gradients for Stochastic Differential Equations (latent SDE) | ◦ preprint | arxiv.org/abs/2001.01328 |
| Neural Controlled Differential Equations for Irregular Time Series | ◦ preprint | arxiv.org/abs/2005.08926 |
| Deep State Space Models for Time Series Forecasting | proceedings.neurips.cc/paper/2018 | |
| Hamiltonian Neural Networks | ◦ preprint | arxiv.org/abs/1906.01563 |
| Lagrangian Neural Networks | ◦ preprint | arxiv.org/abs/2003.04630 |
| Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning | ◦ preprint | arxiv.org/abs/1907.04490 |
| E(n) Equivariant Graph Neural Networks | ◦ preprint | arxiv.org/abs/2102.09844 |
| Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control (SymODEN) | ◦ preprint | arxiv.org/abs/1909.12077 |
| Hamiltonian Generative Networks (HGN) | ◦ preprint | arxiv.org/abs/1909.13789 |
| Dissipative SymODEN: Hamiltonian Dynamics with Dissipation and Control | ◦ preprint | arxiv.org/abs/2002.08860 |
| DeepONet: Learning Nonlinear Operators … | ◦ preprint | arxiv.org/abs/1910.03193 |
| Fourier Neural Operator for Parametric PDEs (FNO) | ◦ preprint | arxiv.org/abs/2010.08895 |
| Neural Operator: Graph Kernel Network for PDEs | ◦ preprint | arxiv.org/abs/2003.03485 |
| Physics-Informed Neural Operator (PINO) | ◦ preprint | arxiv.org/abs/2111.03794 |
| Geometry-Informed Neural Operator for Large-Scale 3D PDEs (GINO) | ◦ preprint | arxiv.org/abs/2309.00583 |
| Clifford Neural Layers for PDE Modeling | ◦ preprint | arxiv.org/abs/2209.04934 |
| Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear PDEs | ◦ preprint | arxiv.org/abs/1711.10561 |
| Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear PDEs | ◦ preprint | arxiv.org/abs/1711.10566 |
| Characterizing possible failure modes in physics-informed neural networks | ✓ peer | arxiv.org/abs/2109.01050 |
| When and why PINNs fail to train: A neural tangent kernel perspective | ◦ preprint | arxiv.org/abs/2007.14527 |
| Variational Physics-Informed Neural Networks (VPINN) | ◦ preprint | arxiv.org/abs/1912.00873 |
| Conservative Physics-Informed Neural Networks (cPINN) on discrete domains | ✓ peer | doi.org/10.1016/j.cma.2020.113028 |
| Feedback Systems: An Introduction for Scientists and Engineers | — | |
| Nonlinear Systems (3e) | — | |
| Dynamic Programming and Optimal Control | — | |
| Modern Control Engineering (5e) | — | |
| Feedback Control of Dynamic Systems (7e) | — | |
| Multivariable Feedback Control: Analysis and Design (2e) | — | |
| Essentials of Robust Control | — | |
| Linear Systems | — | |
| System Identification: Theory for the User (2e) | — | |
| Identification of Dynamic Systems: An Introduction with Applications | — | |
| Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control (2e) | — | |
| Neuro-Dynamic Programming | — | |
| Modern Robotics: Mechanics, Planning, and Control | — | |
| Probabilistic Robotics | — | |
| Nonlinear System Identification: From Classical Approaches to Neural Networks, Fuzzy Models, and Gaussian Processes (2e) | — | |
| Advanced Digital Signal Processing Methods for Filtering, Identification, and Nonlinear Systems Control | — | |
| Learning for Adaptive and Reactive Robot Control: A Dynamical Systems Approach | — | |
| System Dynamics: Modeling, Simulation, and Control of Mechatronic Systems (5e) | — | |
| Introduction to Mechatronic Design | — | |
| Model Predictive Control: Theory, Computation, and Design (2e) | — | |
| The RKHS underlying linear SDE estimation, Kalman filtering and their relation to optimal control | ◦ preprint | arxiv.org/abs/2208.07030 |
| New extension of the Kalman filter to nonlinear systems (UKF) | ✓ peer | doi.org/10.1117/12.280797 |
| The Unscented Kalman Filter for Nonlinear Estimation | — unref | doi.org/10.1109/ASSPCC.2000.882463 |
| Unscented Filtering and Nonlinear Estimation | ✓ peer | doi.org/10.1109/JPROC.2003.823141 |
| The Ensemble Kalman Filter: theoretical formulation & practical implementation (EnKF) | ✓ peer | doi.org/10.1007/s10236-003-0036-9 |
| Novel approach to nonlinear/non-Gaussian Bayesian state estimation (bootstrap particle filter) | ✓ peer | doi.org/10.1049/ip-f-2.1993.0015 |
| A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking | ✓ peer | doi.org/10.1109/78.978374 |
| Maximum Likelihood Estimates of Linear Dynamic Systems (RTS smoother) | ✓ peer | doi.org/10.2514/3.3166 |
| On the Identification of Variances and Adaptive Kalman Filtering (adaptive KF) | ✓ peer | doi.org/10.1109/TAC.1970.1099422 |
| The Interacting Multiple Model Algorithm for Systems with Markovian Switching (IMM) | ✓ peer | doi.org/10.1109/9.1299 |
| Constrained State Estimation for Nonlinear Discrete-Time Systems (moving-horizon estimation) | ✓ peer | doi.org/10.1109/TAC.2002.808470 |
| Three Examples of the Stability Properties of the Invariant Extended Kalman Filter (IEKF) | ✓ peer | doi.org/10.1016/j.ifacol.2017.08.061 |
| Adaptive Switching Circuits (LMS adaptive filter) | — unref | doi.org/10.21236/AD0241531 |
| Application of Statistical Filter Theory to the Optimal Estimation of Position and Velocity On Board a Circumlunar Vehicle (EKF origin) | ntrs.nasa.gov/citations/19620006857 | |
| Applied Optimal Estimation | — | |
| The Iterated Kalman Filter Update as a Gauss–Newton Method (IEKF) | ✓ peer | doi.org/10.1109/9.250476 |
| Cubature Kalman Filters (CKF) | ✓ peer | doi.org/10.1109/TAC.2009.2019800 |
| Nonlinear Bayesian Estimation Using Gaussian Sum Approximations (Gaussian-sum filter) | ✓ peer | doi.org/10.1109/TAC.1972.1100034 |
| Gaussian Filters for Nonlinear Filtering Problems | ✓ peer | doi.org/10.1109/9.855552 |
| Inferring the causes of noise from binary outcomes: A normative theory of learning under uncertainty | doi.org/10.1037/rev0000638 | |
| Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models | arxiv.org/abs/2608.09696 |