Forward modeling is a computational approach used to simulate or predict outcomes based on a set of known initial conditions, parameters, and governing equations. It essentially involves using a model to predict the future behavior of a system or to simulate what we would observe under specific conditions. Here are some key features: 1. **Directionality**: Forward modeling starts with known parameters to predict outcomes, as opposed to inverse modeling, which starts with outcomes to find unknown parameters. 2. **Deterministic or Stochastic**: Depending on the system, forward models can be deterministic, yielding a fixed output for a given input, or stochastic, where output is probabilistic due to inherent uncertainties. 3. **Governing Equations**: These models rely on physical, chemical, or mathematical equations to describe the behavior of the system. 4. **Parameter Input**: Initial conditions and constants are explicitly defined before running the model. 5. **Outcome Prediction**: The goal is often to predict future states of a system or to explore what would happen under different scenarios. 6. **Simplicity or Complexity**: Models can range from simple linear equations to complex, multi-variable systems requiring high-performance computing. 7. **Validation and Calibration**: The models often need to be validated against empirical data and may require calibration to improve their predictive accuracy. 8. **Applications**: Widely used in fields such as climate science, engineering, economics, geophysics, and more. 9. **Uncertainties**: While less prone to uncertainties compared to inverse models, forward models still have limitations due to approximations or incomplete understanding of the system. References: - ["Introduction to Forward Modeling"](https://academic.oup.com/gji/article/176/2/415/650653) - ["Concepts and Applications of Finite Element Analysis"](https://www.wiley.com/en-us/Concepts+and+Applications+of+Finite+Element+Analysis%2C+4th+Edition-p-9780471356059) ![[Pasted image 20231004120444.png]]