![]() ![]() In the real world, this parameter exhibits extreme spatial variability, by many orders of magnitude. A classic example is hydraulic conductivity, a fundamental material property for sediments and rocks, which helps control the rate of water movement in the subsurface. ![]() In reality, physical parameters often significantly vary spatially (e.g., heterogeneity), directionally (i.e., anisotropy), and temporally. Even assuming that the model, inherently a simplification of reality, accurately represents the physics of the system of interest, the model will include physical parameters whose values are often highly uncertain. numerical and analytical models)?Īdvanced physical-based models are based upon equations that explicitly represent the physics of interest. What other advantages do ANNs have over traditional physical-based models (e.g. In addition, unlike numerical models, ANNs can easily be initialized to real-time conditions, improving prediction accuracy. Because of their powerful non-linear modeling capability (see below), ANNs can accurately model highly non-linear and complex phenomena. Additionally, unlike physical-based and statistical models, ANNs are not constrained by simplifying mathematical assumptions (e.g., linear system, normal distribution, etc.) or physical assumptions (e.g., laminar flow). Instead, more easily measurable and less uncertain variables like water levels and air temperature can be used as inputs or predictor variables. Unlike a physical-based (e.g., numerical) model, ANNs do not rely upon governing physical laws (e.g., Conservation of Momentum) and consequently, difficult to estimate parameters (e.g., hydraulic conductivity, streambed thickness, etc.) are typically not required for their development and operation. How are ANN models different from traditional physical-based and statistical models? They have been proven to outperform other advanced modeling techniques in a variety of applications and are used extensively in many sectors, including the military, NASA, and Wall Street. One of the powerful features of ANN technology is that, as more data becomes available, ANN models can easily be updated and improved with additional training to more fully capture subtle tendencies. An ANN, through proper development and training, “learns” the system behavior of interest by processing representative data patterns through its architecture. Frequently Asked Questions What are Artificial Neural Networks?ĪNN technology, a human “learning” paradigm form of artificial intelligence, is a compelling and often superior alternative to physical-based and statistical modeling approaches. ![]()
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