Intro; Contents; About the Editors; 1 Introduction to Dynamic Data Driven Applications Systems; 1.1 Introduction; 1.2 What Is DDDAS?; 1.3 State Estimation and Data Assimilation; 1.3.1 DDDAS and Adaptive State Estimation; 1.3.2 Does DDDAS Use Feedback Control?; 1.4 DDDAS Methods; 1.5 DDDAS Research Areas of Historical Development; 1.5.1 Theory: Modeling and Analysis; 1.5.2 Methods: Domain Applications; 1.5.3 Design: Systems and Architectures; 1.6 Book Overview; 1.7 DDDAS Future; 1.8 Summary; References; Part I Measurement-Aware: Data Assimilation, Uncertainty Quantification

2 Tractable Non-Gaussian Representations in Dynamic Data Driven Coherent Fluid Mapping2.1 Introduction; 2.1.1 Systems Dynamics and Optimization; 2.1.2 Dynamically Deformable Reduced Models; 2.1.3 Nonlinear High Dimensional Inference; 2.2 Ensemble Learning in Mixture Ensembles; 2.2.1 Mixture Ensemble Filter and Smoother; 2.3 Nonlinear Filtering Must Reduce Total Variance; 2.4 Ensemble Learning with a Stacked Cascade; 2.4.1 Application Example; 2.5 Information Theoretic Learning in Filtering; 2.5.1 Tractable Information Theoretic Approach; 2.6 Application Example; 2.7 Conclusions; References

3 Dynamic Data-Driven Adaptive Observations in Data Assimilation for Multi-scale Systems3.1 Introduction; 3.2 Dimensional Reduction and Homogenization; 3.3 Data Assimilation in Multi-scale Systems; 3.4 Information-Theoretic Sensor Selection Strategy; 3.4.1 The Linear Case; 3.4.2 Information Flow for the Coarse Grained Dynamics; 3.4.3 Finite-Time Lyapunov Exponents and Singular Vectors; 3.4.4 Sensor Selection and the Lorenz 1963 Model; 3.4.4.1 Sensor Selection with Kullback-Leibler Divergence; 3.4.4.2 Sensor Selection with Singular Vectors

3.4.4.3 Influence of Singular Values in Discrete-Time, Linear Gaussian Case3.4.4.4 Numerical Results; 3.5 Conclusions; References; 4 Dynamic Data-Driven Uncertainty Quantification via Polynomial Chaos for Space Situational Awareness; 4.1 Introduction; 4.2 Gaussian Mixture Models; 4.3 Polynomial Chaos; 4.4 Polynomial Chaos with Gaussian Mixture Models; 4.5 Global Ionosphere-Thermosphere Model; 4.6 Results; 4.6.1 Orbital Uncertainty Quantification; 4.6.2 Initial Results for Atmospheric Density Forecasting; 4.7 Conclusion; References; Part II Signals-Aware: Process Monitoring

5 Towards Learning Spatio-Temporal Data Stream Relationships for Failure Detection in Avionics5.1 Introduction; 5.2 Background; 5.2.1 Error Detection and Correction Methods; 5.2.2 Spatio-Temporal Data Stream Processing System; 5.3 Design of Machine Learning Component; 5.3.1 Prediction in PILOTS Programming Language; 5.3.2 Prediction in PILOTS Runtime; 5.4 Data-Driven Learning of Linear Models; 5.4.1 Learning Algorithm; 5.4.2 Linear Model Accuracy; 5.5 Statistical Learning of Dynamic Models; 5.5.1 Offline Supervised Learning; 5.5.1.1 Gaussian Naïve Bayes Classifiers