Artificial Intelligence in Physical Sciences: Symbolic Regression Trends and Perspectives Archives of Computational Methods in Engineering

In the implicit method, no optimization of calibration parameters occurs as they undergo a tree-based GP procedure on the first steps. In addition, no extra combination of expressions similar to the explicit method are performed, as they are already combined in a multi-tree GP, where each individual has a number of trees that correspond to the number of calibration parameters. Note, that the authors recommend the implicit method for further use, while they also note that although the implicit method may be more computationally expensive, the remarkably higher accuracy cannot be ignored. In contrast to other hydrocarbon-based materials (e.g., oil, coal), natural gas constitutes a cheaper and cleaner option [178] to meet our energy demands. Similar to petroleum engineering, estimating the viscosity is one of the top priorities in natural gas studies, as it can be utilized to efficiently synthesize models about production, transportation or gas storage systems [179]. To this end, SR studies regarding the prediction of dynamic viscosity [180] or pure and impure viscosity [179] appear most appealing.

These soft reads and writes form a bottleneck when implemented in the conventional von Neumann architectures (e.g., CPUs and GPUs), especially for AI models demanding over millions of memory entries. Thanks to the high-dimensional geometry of our resulting vectors, their real-valued components can be approximated by binary, or bipolar components, taking up less storage. More importantly, this opens the door for efficient realization using analog in-memory computing. During training and inference using such an AI system, the neural network accesses the explicit memory using expensive soft read and write operations. Controversies arose from early on in symbolic AI, both within the field—e.g., between logicists (the pro-logic “neats”) and non-logicists (the anti-logic “scruffies”)—and between those who embraced AI but rejected symbolic approaches—primarily connectionists—and those outside the field. Critiques from outside of the field were primarily from philosophers, on intellectual grounds, but also from funding agencies, especially during the two AI winters.

Machine Learning

He gave a talk at an AI workshop at Stanford comparing symbols to aether, one of science’s greatest mistakes. Multiple different approaches to represent knowledge and then reason with those representations have been investigated. Below is a quick overview of approaches to knowledge representation and automated reasoning. Its history was also influenced by Carl Hewitt’s PLANNER, an assertional database with pattern-directed invocation of methods.

Data from astronomical observations is undoubtedly rich and AI methods are well-posed to its exploitation. For example, galaxy clusters turn out to be the most immense structures in the universe [197], as they contain several galaxies, that further include dark matter, black holes and more [198]. Moreover, they operate by mechanisms regarding the evolution and formation of those, whose details are not yet fully understood [198]. In the explicit method, the calibration parameters are primarily optimized, while a formula for the prediction of the optimized values using SR, is generated next. Then, a combination of the generated expressions on calibration parameters and a physics-based constitutive model takes place, in order to create a hybrid approach.

Code, Data and Media Associated with this Article

Symbols also serve to transfer learning in another sense, not from one human to another, but from one situation to another, over the course of a single individual’s life. That is, a symbol offers a level of abstraction above the concrete and granular details of our sensory experience, an abstraction that allows us to transfer what we’ve learned in one place to a problem we may encounter somewhere else. In a certain sense, every abstract category, like chair, asserts an analogy between all the disparate symbolic machine learning objects called chairs, and we transfer our knowledge about one chair to another with the help of the symbol. If machine learning can appear as a revolutionary approach at first, its lack of transparency and a large amount of data that is required in order for the system to learn are its two main flaws. Companies now realize how important it is to have a transparent AI, not only for ethical reasons but also for operational ones, and the deterministic (or symbolic) approach is now becoming popular again.

• Special quantization methods may need to be developed to facilitate this in future work in order to fully take advantage of hyperdimensional representations.
• This review has been focused on presenting an ML-based method, Symbolic Regression (SR), which has been developed on Evolutionary Computing principles.
• So how do we make the leap from narrow AI systems that leverage reinforcement learning to solve specific problems, to more general systems that can orient themselves in the world?
• A way to visualize symbolic expressions is by a tree-structure form that contains primitive functions and terminal constants.
• Symbolic regression (SR) is a machine learning-based regression method based on genetic programming principles that integrates techniques and processes from heterogeneous scientific fields and is capable of providing analytical equations purely from data.