AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity
- Interpretability and Transparency: As models grow in sophistication and autonomy, ensuring their interpretability, especially in critical domains (energy, health, finance), is necessary for trust and regulatory acceptance [6].
Conflicts of Interest
List of Contributions
- Bashishtha, T.; Singh, V.; Yadav, U.; Varshney, T. Reaction Curve-Assisted Rule-Based PID Control Design for Islanded Microgrid. Energies 2024, 17, 1110. https://doi.org/10.3390/en17051110.
- Tian, F.; Wang, Y.; Li, Z. Numerical Simulation of Soliton Propagation Behavior for the Fractional-in-Space NLSE with Variable Coefficients on Unbounded Domain. Fractal Fract. 2024, 8, 163. https://doi.org/10.3390/fractalfract8030163.
- Shen, H.; Shan, X. An Efficient Image Cryptosystem Utilizing Difference Matrix and Genetic Algorithm. Entropy 2024, 26, 351. https://doi.org/10.3390/e26050351.
- Zhang, J.; Shi, B.; Wang, B.; Yu, G. Crushing Response and Optimization of a Modified 3D Re-Entrant Honeycomb. Materials 2024, 17, 2083. https://doi.org/10.3390/ma17092083.
- Du, X.; Salasakar, S.; Thakur, G. A Comprehensive Summary of the Application of Machine Learning Techniques for CO2-Enhanced Oil Recovery Projects. Mach. Learn. Knowl. Extr. 2024, 6, 917–943. https://doi.org/10.3390/make6020043.
- El Sayed, A.; Poyrazoglu, G. Analysis of Grid Performance with Diversified Distributed Resources and Storage Integration: A Bilevel Approach with Network-Oriented PSO. Energies 2024, 17, 2270. https://doi.org/10.3390/en17102270.
- Belhaiza, S.; Al-Abdallah, S. A Neural Network Forecasting Approach for the Smart Grid Demand Response Management Problem. Energies 2024, 17, 2329. https://doi.org/10.3390/en17102329.
- Zhang, T.; Mo, H. Towards Multi-Objective Object Push-Grasp Policy Based on Maximum Entropy Deep Reinforcement Learning under Sparse Rewards. Entropy 2024, 26, 416. https://doi.org/10.3390/e26050416.
- Hamidpour, P.; Araee, A.; Baniassadi, M.; Garmestani, H. Multiphase Reconstruction of Heterogeneous Materials Using Machine Learning and Quality of Connection Function. Materials 2024, 17, 3049. https://doi.org/10.3390/ma17133049.
- Neugebauer, M.; d’Obyrn, J.; Sołowiej, P. Economic Analysis of Profitability of Using Energy Storage with Photovoltaic Installation in Conditions of Northeast Poland. Energies 2024, 17, 3075. https://doi.org/10.3390/en17133075.
- Liu, M.; Cao, Y.; Nie, C.; Wang, Z.; Zhang, Y. Finite Element Analysis of Densification Process in High Velocity Compaction of Iron-Based Powder. Materials 2024, 17, 3085. https://doi.org/10.3390/ma17133085.
- De-la-Mata-Moratilla, S.; Gutierrez-Martinez, J.; Castillo-Martinez, A.; Caro-Alvaro, S. Prediction of the Behaviour from Discharge Points for Solid Waste Management. Mach. Learn. Knowl. Extr. 2024, 6, 1389–1412. https://doi.org/10.3390/make6030066.
- Bassetti, D.; Pospíšil, L.; Horenko, I. On Entropic Learning from Noisy Time Series in the Small Data Regime. Entropy 2024, 26, 553. https://doi.org/10.3390/e26070553.
- Shiryayeva, O.; Suleimenov, B.; Kulakova, Y. Optimal Design of I-PD and PI-D Industrial Controllers Based on Artificial Intelligence Algorithm. Algorithms 2024, 17, 288. https://doi.org/10.3390/a17070288.
- Mahammedi, A.; Kouider, R.; Tayeb, N.; Kassir Al-Karany, R.; Cuerda-Correa, E.; Al-Kassir, A. Thermal and Hydrodynamic Measurements of a Novel Chaotic Micromixer to Enhance Mixing Performance. Energies 2024, 17, 3248. https://doi.org/10.3390/en17133248.
- Maciorowski, D.; Spychala, M.; Miedzinska, D. An Experimental and Numerical Investigation of a Heat Exchanger for Showers. Energies 2024, 17, 3641. https://doi.org/10.3390/en17153641.
- Ktari, A.; Ghauch, H.; Rekaya-Ben Othman, G. Machine Learning Techniques for Blind Beam Alignment in mmWave Massive MIMO. Entropy 2024, 26, 626. https://doi.org/10.3390/e26080626.
- López, J.; Vásquez-Coronel, J. Analyzing Monofractal Short and Very Short Time Series: A Comparison of Detrended Fluctuation Analysis and Convolutional Neural Networks as Classifiers. Fractal Fract. 2024, 8, 460. https://doi.org/10.3390/fractalfract8080460.
- Ren, Y.; Zhang, Z.; He, G.; Zhang, Y.; Zhang, Z. Hierarchical Significance of Environment Impact Factor on the Sand Erosion Performance of Lightweight Alloys. Materials 2024, 17, 3890. https://doi.org/10.3390/ma17163890.
- Shen, H.; Kulasegaram, S.; Brousseau, E. On the Aptness of Material Constitutive Models for Simulating Nano-Scratching Processes. Materials 2024, 17, 4208. https://doi.org/10.3390/ma17174208.
- Yu, Y.; Zhou, D.; Qiao, L.; Feng, P.; Kang, X.; Yang, C. Highly Porous Co-Al Intermetallic Created by Thermal Explosion Using NaCl as a Space Retainer. Materials 2024, 17, 4380. https://doi.org/10.3390/ma17174380.
- Lee, M. Fractal Self-Similarity in Semantic Convergence: Gradient of Embedding Similarity across Transformer Layers. Fractal Fract. 2024, 8, 552. https://doi.org/10.3390/fractalfract8100552.
- Fernández Valderrama, D.; Guerrero Alonso, J.; León de Mora, C.; Robba, M. Scenario Generation Based on Ant Colony Optimization for Modelling Stochastic Variables in Power Systems. Energies 2024, 17, 5293. https://doi.org/10.3390/en17215293.
- Cai, W.; Di, X.; Wang, X.; Gao, W.; Jia, H. Stealthy Vehicle Adversarial Camouflage Texture Generation Based on Neural Style Transfer. Entropy 2024, 26, 903. https://doi.org/10.3390/e26110903.
- Hui, P.; Zhao, J.; Li, C.; Zhu, Q. Quotient Network-A Network Similar to ResNet but Learning Quotients. Algorithms 2024, 17, 521. https://doi.org/10.3390/a17110521.
- Kantamneni, S.; Liu, Z.; Tegmark, M. How Do Transformers Model Physics? Investigating the Simple Harmonic Oscillator. Entropy 2024, 26, 997. https://doi.org/10.3390/e26110997.
- Lee, M.; Lee, S. Box-Counting Dimension Sequences of Level Sets in AI-Generated Fractals. Fractal Fract. 2024, 8, 730. https://doi.org/10.3390/fractalfract8120730.
- Belfekih, T.; Fitas, R.; Schaffrath, H.; Schabel, S. Graph-Based Analysis for the Characterization of Corrugated Board Compression. Materials 2024, 17, 6083. https://doi.org/10.3390/ma17246083.
- Kang, S.; Choi, D.; Son, S.; Choi, C. Generation and Validation of CFD-Based ROMs for Real-Time Temperature Control in the Main Control Room of Nuclear Power Plants. Energies 2024, 17, 6406. https://doi.org/10.3390/en17246406.
- Benaissa, B.; Kobayashi, M.; Takenouchi, H. Enhancing Consumer Agent Modeling Through Openness-Based Consumer Traits and Inverse Clustering. Mach. Learn. Knowl. Extr. 2025, 7, 9. https://doi.org/10.3390/make7010009.
- Vaiyapuri, T. Optimizing Hydrogen Production in the Co-Gasification Process: Comparison of Explainable Regression Models Using Shapley Additive Explanations. Entropy 2025, 27, 83. https://doi.org/10.3390/e27010083.
- Huang, L.; Cao, Y.; Zhang, M.; Meng, Z.; Wang, T.; Zhu, X. Optimization Design of Casting Process for Large Long Lead Cylinder of Aluminum Alloy. Materials 2025, 18, 531. https://doi.org/10.3390/ma18030531.
- Zhang, H.; Liu, H. Real-Time Power System Optimization Under Typhoon Weather Using the Smart “Predict, Then Optimize” Framework. Energies 2025, 18, 615. https://doi.org/10.3390/en18030615.
- Zhang, E.; Chen, X.; Zhou, J.; Wu, H.; Chen, Y.; Huang, H.; Li, J.; Yang, Q. Modeling the Carbothermal Chlorination Mechanism of Titanium Dioxide in Molten Salt Using a Deep Neural Network Potential. Materials 2025, 18, 659. https://doi.org/10.3390/ma18030659.
- Soto Calvo, M.; Lee, H. Electrical Storm Optimization (ESO) Algorithm: Theoretical Foundations, Analysis, and Application to Engineering Problems. Mach. Learn. Knowl. Extr. 2025, 7, 24. https://doi.org/10.3390/make7010024.
- Xu, X.; Lu, X.; Wang, J. DeeWaNA: An Unsupervised Network Representation Learning Framework Integrating Deepwalk and Neighborhood Aggregation for Node Classification. Entropy 2025, 27, 322. https://doi.org/10.3390/e27030322.
- Tijani, O.; Serra, S.; Lanusse, P.; Malti, R.; Viot, H.; Reneaume, J. Grey-Box Modelling of District Heating Networks Using Modified LPV Models. Energies 2025, 18, 1626. https://doi.org/10.3390/en18071626.
- Ugwumadu, C.; Tabarez, J.; Drabold, D.; Pandey, A. PowerModel-AI: A First On-the-Fly Machine-Learning Predictor for AC Power Flow Solutions. Energies 2025, 18, 1968. https://doi.org/10.3390/en18081968.
- Rivera Torres, P.; Chen, C.; Rodríguez González, S.; Llanes Santiago, O. A Learning Probabilistic Boolean Network Model of a Manufacturing Process with Applications in System Asset Maintenance. Entropy 2025, 27, 463. https://doi.org/10.3390/e27050463.
- Levner, E.; Kriheli, B. Analysis of Reliability and Efficiency of Information Extraction Using AI-Based Chatbot: The More-for-Less Paradox. Algorithms 2025, 18, 412. https://doi.org/10.3390/a18070412.
References
- Peng, Y.; Chen, C.-H.; Fu, M.C. A Review of Simulation Optimization with Connection to Artificial Intelligence. Fundam. Res. 2025. ISSN: 2667-3258. [Google Scholar] [CrossRef]
- Ye, Y.; Pandey, A.; Bawden, C.; Sumsuzzman, D.M.; Rajput, R.; Shoukat, A.; Singer, B.H.; Moghadas, S.M.; Galvani, A.P. Integrating artificial intelligence with mechanistic epidemiological modeling: A scoping review of opportunities and challenges. Nat. Commun. 2025, 16, 581. [Google Scholar] [CrossRef] [PubMed]
- Karpatne, A.; Deshwal, A.; Jia, X.; Ding, W.; Steinbach, M.; Zhang, A.; Kumar, V. AI-enabled scientific revolution in the age of generative AI: Second NSF workshop report. npj Artif. Intell. 2025, 1, 18. [Google Scholar] [CrossRef]
- Kang, N. Generative AI-driven design optimization: Eight key application scenarios. JMST Adv. 2025, 7, 105–111. [Google Scholar] [CrossRef]
- Shadkam, E.; Irannezhad, E. A comprehensive review of simulation optimization methods in agricultural supply chains and transition towards an agent-based intelligent digital framework for agriculture 4.0. Eng. Appl. Artif. Intell. 2025, 143, 109930, ISSN: 0952-1976. [Google Scholar] [CrossRef]
- Tundwal, A.; Kaur, M.; Kaur, D. Advances in Artificial Intelligence and Computational Methods: Enhancing Modeling, Prediction, and Optimization. Int. J. Res. Appl. Sci. Eng. Technol. 2025, 13, 901–909, ISSN: 2321-9653. [Google Scholar] [CrossRef]
- Roberto, I.; Hocine, C. Editorial: Interdisciplinary approaches to complex systems: Highlights from FRCCS 2023/24. Front. Big Data 2025, 8, 2. [Google Scholar] [CrossRef] [PubMed]
- Fustolo-Gunnink, S.F.; de Boode, W.P.; Dekkers, O.M.; Greisen, G.; Lopriore, E.; Russo, F. If things were simple, word would have gotten around. Can complexity science help us improve pediatric research? Pediatr. Res. 2024. [Google Scholar] [CrossRef] [PubMed]
- Krzywanski, J.; Wesolowska, M.; Blaszczuk, A.; Majchrzak, A.; Komorowski, M.; Nowak, W. The Non-Iterative Estimation of Bed-to-Wall Heat Transfer Coefficient in a CFBC by Fuzzy Logic Methods. Procedia Eng. 2016, 157, 66–71. [Google Scholar] [CrossRef]
- Błaszczuk, A.; Krzywański, J. A comparison of fuzzy logic and cluster renewal approaches for heat transfer modeling in a 1296 t/h CFB boiler with low level of flue gas recirculation. Arch. Thermodyn. 2017, 38, 91–122. [Google Scholar] [CrossRef]
- Maslej, N.; Fattorini, L.; Perrault, R.; Gil, Y.; Parli, V.; Kariuki, N.; Capstick, E.; Reuel, A.; Brynjolfsson, E.; Etchemendy, J.; et al. The AI Index 2025 Annual Report. AI Index Steering Committee, Institute for Human-Centered AI; Stanford University: Stanford, CA, USA, 2025. [Google Scholar]
Journal | No. of Papers |
---|---|
Energies | 11 |
Entropy | 9 |
Materials | 9 |
Fractal and Fractional | 4 |
Machine Learning & Knowledge Extraction (MAKE) | 4 |
Algorithms | 3 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Sosnowski, M.; Krzywanski, J.; Grabowska, K.; Skrobek, D.; Uddin, G.M.; Kozlowski, C.; Przybylski, J.; Deska, M. AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity. Materials 2025, 18, 4112. https://doi.org/10.3390/ma18174112
Sosnowski M, Krzywanski J, Grabowska K, Skrobek D, Uddin GM, Kozlowski C, Przybylski J, Deska M. AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity. Materials. 2025; 18(17):4112. https://doi.org/10.3390/ma18174112
Chicago/Turabian StyleSosnowski, Marcin, Jaroslaw Krzywanski, Karolina Grabowska, Dorian Skrobek, Ghulam Moeen Uddin, Cezary Kozlowski, Jacek Przybylski, and Malgorzata Deska. 2025. "AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity" Materials 18, no. 17: 4112. https://doi.org/10.3390/ma18174112
APA StyleSosnowski, M., Krzywanski, J., Grabowska, K., Skrobek, D., Uddin, G. M., Kozlowski, C., Przybylski, J., & Deska, M. (2025). AI and Computational Methods for Modelling, Simulations and Optimizing of Advanced Systems: Innovations in Complexity. Materials, 18(17), 4112. https://doi.org/10.3390/ma18174112