There is a newer version of the record available.

Published August 19, 2025 | Version 0.9

Calculus of Element-Generated Sets (CEGS): A Symbolic Framework for Mathematics, Physics, and AI

Description

Description

This monograph introduces the Calculus of Element-Generated Sets (CEGS), a symbolic extension of mathematical analysis. Part I develops the mathematical foundations of CEGS, including symbolic identity sets, symbolic closure, divergence resolution, and functional iteration. Part II applies these tools to physics, with constructions for non-symmetric potentials, displacement and excitation operators, anisotropic Gaussian fields, and constraint projection methods. Part III explores applications in computer science and AI, including symbolic reasoning, algebraic solvers, inverse reasoning operators, and multivalued symbolic computation. Together these developments provide a coherent and deterministic symbolic framework, with implications for mathematics, physical modeling, and intelligent systems.

 

Abstract

The Calculus of Element-Generated Sets (CEGS) develops a symbolic framework that extends traditional mathematical analysis. CEGS introduces symbolic sets and symbolic algebraic operations that preserve structural information beyond what classical functions can express. The framework resolves indeterminate and divergent forms, defines symbolic identity sets, and generalizes functional iteration. This symbolic foundation enables consistent treatment of discontinuities, infinite expansions, and structural equivalences.

Applications of CEGS extend across mathematics, physics, and computer science. In physics, symbolic operators model non-symmetric potential fields, constraint projection, and anisotropic Gaussian structures. The framework also reformulates Fourier synthesis in symbolic terms and provides deterministic approaches to particle physics phenomena where incompleteness arises in conventional models. In computer science and AI, CEGS establishes symbolic reasoning and inverse operators for problem decomposition, explainability, and multivalued reasoning.

CEGS thus provides a unifying symbolic structure for analysis, modeling, and computation, bridging foundational mathematics with applied domains.

Remark

The function used in Chapter 29 and 30 appears to be incorrect and the chapters need an overhaul. However from a didactic point of view the application of the method is correct. Only the functions and images would require a correction.
For a corrected version see the emitting framework about time space oscillations https://zenodo.org/communities/tso-research. Especially TSO & Quantum Mechanics.

Files

CEGS.pdf

Files (3.9 MB)

Name Size Download all
md5:ef1aa7a696eb302cc03262439d6e9f3f
3.9 MB Preview Download

Additional details

Dates

Other
2025-08-19
First uplate

References

  • ] D. J. Griffiths, Introduction to Quantum Mechanics, 2nd ed., Pearson Prentice Hall, 2005. [2] J. V. Jelley, Cerenkov Radiation and its Applications, Pergamon Press, 1958. [3] J. J. Sakurai and Jim Napolitano, Modern Quantum Mechanics, 2nd Edition, Addison-Wesley, 2010. [4] Claude Cohen-Tannoudji, Bernard Diu, Franck Laloe, Quantum Mechanics, Volume 1, Revised Edition, Wiley-VCH, 2005. [5] Gilbert Strang, Introduction to Linear Algebra, 6th edition, Wellesley–Cambridge Press, 2023. [6] Sheldon Axler, Linear Algebra Done Right, 3rd Edition, Springer, 2015. [7] Roger A. Horn and Charles R. Johnson, Matrix Analysis, 2nd Edition, Cambridge University Press, 2012. [8] Serge Lang, Linear Algebra, Undergraduate Texts in Mathematics, Springer, 1987. [9] Riemann, B. ¨Uber die Darstellbarkeit einer Funktion durch eine trigonometrische Reihe. Habilitationss- chrift, G¨ottingen, 1854. [10] Rudin, W. Principles of Mathematical Analysis. 3rd Edition, McGraw-Hill, 1976. [11] Knopp, K. Theory and Application of Infinite Series. Dover Publications, 1990. [12] T. Needham, Visual Complex Analysis, Clarendon Press, 1997. [13] L. V. Ahlfors, Complex Analysis, 3rd edition, McGraw-Hill, 1979. [14] J. B. Conway, Functions of One Complex Variable I, Springer, 1973. [15] A. F. Beardon, The Geometry of Discrete Groups, Springer, 1983. [16] H. M. Edwards, Riemann's Zeta Function, Dover Publications, 2001. [17] S. J. Patterson, An Introduction to the Theory of the Riemann Zeta-Function, Cambridge University Press, 1988. [18] B. C. Berndt, Ramanujan's Notebooks: Part I, Springer, 1985. [19] R. Kreminski, Numerical evaluation of the Stieltjes constants, Mathematics of Computation, vol. 72, no. 242, pp. 1379–1397, 2003. [20] A. Ivic, The Theory of Hardy's Z-Function, Cambridge University Press, 2013. [21] Michael Sipser. Introduction to the Theory of Computation. 3rd Edition, Cengage Learning, 2012. https://www.cengage.com/c/introduction-to-the-theory-of-computation-3e-sipser/ 9781133187790/ [22] Martin Davis. Computability and Unsolvability. Dover Publications, 1982. https://store. doverpublications.com/0486614719.html 307 308 BIBLIOGRAPHY [23] Piergiorgio Odifreddi. Classical Recursion Theory. North-Holland, 1989. https://www.elsevier.com/ books/classical-recursion-theory/odifreddi/978-0-444-87295-4 [24] Roger Penrose. The Emperor's New Mind: Concerning Computers, Minds, and the Laws of Physics. Oxford University Press, 1989. https://global.oup.com/academic/product/ the-emperors-new-mind-9780198784920 [25] Penrose, R. (1989). The Emperor's New Mind. Oxford University Press. [26] Chaitin, G. J. (2005). Meta Math! The Quest for Omega. Pantheon Books. [27] Awodey, S. Category Theory. Oxford University Press, 2010. [28] Riehl, E. Category Theory in Context. Dover Publications, 2016. [29] Lawvere, F.W. and Schanuel, S.H. Conceptual Mathematics: A First Introduction to Categories. Cam- bridge University Press, 2009. [30] Artin, E. The Gamma Function. Holt, Rinehart and Winston, 1964. [31] Sumit Gulwani, Oleksandr Polozov, and Rishabh Singh. Program synthesis. Foundations and Trends in Programming Languages, 4(1-2):1–119, 2017. [32] Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B. Tenenbaum, and Jiajun Wu. The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision. In International Conference on Learning Representations (ICLR), 2019. [33] Amanpreet Singh Verma, Miltos Allamanis, and Charles Sutton. Programmatically interpreting neural networks. In International Conference on Learning Representations (ICLR), 2019. [34] D. S. Dummit and R. M. Foote, Abstract Algebra, 3rd Edition, Wiley, 2004. ISBN: 978-0471433347. [35] S. Mac Lane, Categories for the Working Mathematician, 2nd Edition, Springer, 1998. ISBN: 978- 0387984036. https://doi.org/10.1007/978-1-4612-9839-7 [36] A. Einstein, B. Podolsky, and N. Rosen, "Can Quantum-Mechanical Description of Physical Reality Be Considered Complete?" Physical Review, vol. 47, no. 10, pp. 777–780, 1935. doi:10.1103/PhysRev.47.777. [37] N. Brunner, D. Cavalcanti, S. Pironio, V. Scarani, and S. Wehner, "Bell nonlocality," Reviews of Modern Physics, vol. 86, no. 2, pp. 419–478, 2014. doi:10.1103/RevModPhys.86.419. [38] G. de L'Hˆopital, Analyse des Infiniment Petits pour l'Intelligence des Lignes Courbes, Paris, 1696. [39] T. M. Apostol, Mathematical Analysis, Addison-Wesley, 2nd edition, 1974. [40] J. Stewart, Calculus: Early Transcendentals, Cengage Learning, 8th edition, 2016. [41] Walter Rudin, Functional Analysis, McGraw-Hill, 1991. [42] Manuel Bronstein, Symbolic Integration I: Transcendental Functions, Springer, 2005. 2nd edition [43] Barry M. Trager, PhD thesis: Symbolic Integration and Symbolic Linear Algebra, MIT 1984 [44] R. Devaney, A First Course in Chaotic Dynamical Systems, Addison-Wesley, 1992. [45] H.-O. Peitgen, P. Richter, The Beauty of Fractals, Springer, 1986. [46] F. Balibrea-Iniesta et al., "Julia Sets and Lagrangian Descriptors", arXiv:2001.08937. [47] G. Rochon, "Julia Sets in Bicomplex and Quaternionic Spaces", Fractals, World Scientific, 2013. [48] Math StackExchange: https://math.stackexchange.com/questions/4020093 BIBLIOGRAPHY 309 [49] G. H. Hardy, Divergent Series, Oxford University Press, 1949. [50] J. ´Ecalle, Les Fonctions R´esurgentes, Publications Math´ematiques d'Orsay, 1981. [51] S. Mac Lane, Categories for the Working Mathematician, Springer-Verlag, 1971. [52] E. Borel, Le¸cons sur les s´eries divergentes, Gauthier-Villars, 1901. [53] Hall, B.C., Lie Groups, Lie Algebras, and Representations, Springer, 2015. [54] Hestenes, D., New Foundations for Classical Mechanics, Springer, 1999. [55] Lasenby, A., Doran, C., Gull, S., Gravity, Gauge Theories and Geometric Algebra, Phil. Trans. R. Soc. Lond. A (1998). [56] Rovelli, C., Quantum Gravity, Cambridge University Press, 2004. [57] J. H. Conway and D. A. Smith, On Quaternions and Octonions: Their Geometry, Arithmetic, and Symmetry, A K Peters, 2013. [58] J. C. Baez, "The Octonions," Bulletin of the American Mathematical Society, vol. 39, no. 2, pp. 145–205, 2002. [59] R. Penrose, The Road to Reality: A Complete Guide to the Laws of the Universe, Jonathan Cape, 2004. [60] L. Smolin, Three Roads to Quantum Gravity, Basic Books, 2002. [61] A. Zee, Quantum Field Theory in a Nutshell, 2nd ed., Princeton University Press, 2010. [62] J. Schwinger, "On gauge invariance and vacuum polarization," *Phys. Rev.*, vol. 82, pp. 664–679, 1951. [63] J. I. Cirac and P. Zoller, "Goals and opportunities in quantum simulation," *Nature Physics*, vol. 8, pp. 264–266, 2012. [64] Besold, T. R., Garcez, A. d., Bader, S., Bowman, H., Domingos, P., Hitzler, P., ... and van Harmelen, F. (2017). Neural-symbolic learning and reasoning: A survey and interpretation. In *Philosophical Trans- actions of the Royal Society A: Mathematical, Physical and Engineering Sciences*, 375(2104), 20160344. https://doi.org/10.1098/rsta.2016.0344 [65] Garcez, A. d., Lamb, L. C., and Gori, M. (2019). Neural-symbolic computing: An effective methodology for principled integration of machine learning and reasoning. In *FLAP – Journal of Foundations and Trends in Artificial Intelligence*, 13(5–6), 513–612. https://doi.org/10.1561/2400000035 [66] d'Avila Garcez, A., Lamb, L. C., and Serafini, L. (2021). Symbolic–neural learning: A survey and interpretation. In *Proceedings of the AAAI Conference on Artificial Intelligence*, 35(9), 7548–7556. https://doi.org/10.1609/aaai.v35i9.16977 [67] D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning representations by back-propagating errors. Nature, 323(6088):533–536, 1986. [68] I. Goodfellow, Y. Bengio, and A. Courville. Deep Learning. MIT Press, 2016. [69] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recog- nition. Proceedings of the IEEE, 86(11):2278–2324, 1998. [70] W. Rawat and Z. Wang. Deep convolutional neural networks for image classification: A comprehensive review. Neural Computation, 29(9):2352–2449, 2017. [71] S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural Computation, 9(8):1735–1780, 1997. 310 BIBLIOGRAPHY [72] K. Cho, B. van Merri¨enboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078, 2014. [73] T. Kohonen. Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1):59–69, 1982. [74] D. S. Broomhead and D. Lowe. Radial basis functions, multi-variable functional interpolation and adaptive networks. Royal Signals and Radar Establishment, Technical Report, 1988. [75] J.-S. R. Jang. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics, 23(3):665–685, 1993. [76] L. A. Zadeh. The concept of a linguistic variable and its application to approximate reasoning—I. Information Sciences, 8(3):199–249, 1975. [77] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems (NeurIPS), pages 5998–6008, 2017. [78] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Min- derer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.