Evaluating Surrogate Models for Robot Swarm Simulations
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-34020-8_17
Reference24 articles.
1. Alizadeh, R., Allen, J.K., Mistree, F.: Managing computational complexity using surrogate models: a critical review. Res. Eng. Des. 31(3), 275–298 (2020). https://doi.org/10.1007/s00163-020-00336-7
2. Asher, M.J., Croke, B.F.W., Jakeman, A.J., Peeters, L.J.M.: A review of surrogate models and their application to groundwater modeling. Water Resour. Res. 51(8), 5957–5973 (2015). https://doi.org/10.1002/2015WR016967
3. Benítez-Hidalgo, A., Nebro, A.J., García-Nieto, J., Oregi, I., Ser, J.D.: jMetalPy: a python framework for multi-objective optimization with metaheuristics. Swarm Evolut. Comput. 100598 (2019). https://doi.org/10.1016/j.swevo.2019.100598
4. Cheng, S., Prentice, I.C., Huang, Y., Jin, Y., Guo, Y.K., Arcucci, R.: Data-driven surrogate model with latent data assimilation: Application to wildfire forecasting. J. Comput. Phys. 464, 111302 (2022). https://doi.org/10.1016/j.jcp.2022.111302
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