Goals¶
- Objective
- Task
Estimation vs Learning¶
Using models as a means of estimating or predicting a QoI.
Using models as a means of learning.
Modeling & Simulation¶
- Earth System
- Abstraction -> Model
- Measurements -> Data
- Model -> Predictions
Hierarchical Assemblage of Hypothesis¶
- Conservation Laws
- System Discretization
- Process Parameterization
- Uncertainty Quantification
- Solution Procedure
- Sensitivity Analysis
- Iterative Hypothesis Refinement
ML4EO¶
Geo Tasks¶
- Interpolation - Missingness
- Extrapolation - Data Drift, Distribution Shift, Bad Generalization
- Variable Transformation - Multivariate, High Correlation
- Feature Extraction - Ad-hoc, Foundation Models, Downstream Tasks, Transfer Learning
Geo Issues¶
- Measurements
- Data Shape
- Model
- Solution Procedure
Geo Operations¶
Game of Spatiotemporal Dependencies¶
- IID
- Partial
- Global
- Autoregressive
Operator Learning¶
- Space
- Time
- Quantity
- Shape
- Transformation
Hierarchical Sequence of Decisions¶
Data-Driven Modeling Elements¶
- Measurements
- State
- Quantity of Interest
- Latent Variable
Bayesian Modeling¶
- Data Likelihood
- Prior
- Posterior
- Marginal Likelihood
- Prior Prediction
- Posterior Prediction
- Sampling
- Inference
Discretization¶
- Regular
- Rectilinear
- Curvilinear
- Unstructured - FEM/FVM/GNNs (line segments, triangles, quadrilateral, tetrahedral)
- Point Clouds
Parameterization Complexity¶
Parametric
- Linear
- Basis Function
- Neural Network
Functional
- Neural Fields
- Deep ONets
- Neural Operators
Machine Learning Abstractions¶
- Data Module
- Model
- Criteria
- Optimizer
- Learner
Model Form¶
- Parametric
- Generative
- Conditional Parametric
- Conditional Generative
- Dynamical
Software¶
- Hardware Agnostic Tensor Libraries
- AutoDifferentiation
- Deep Learning
- Probabilistic Programming Libraries
- Data Pipelines