Eric Laloy

Person

Laloy, Eric

  • Engineered and Geosystems Analysis

Person: Employee

  1. 2019
  2. Gradient-based deterministic inversion of geophysical data with generative adversarial networks: Is it feasible?

    Laloy, E., Linde, N., Ruffino, C., Hérault, R. C., Gasso, G. & Jacques, D., 1 Dec 2019, In : Computers and Geosciences. 133, p. 1-12 12 p., 104333.

    Research output: Contribution to journalArticle

  3. Nested multiresolution hierarchical simulated annealing algorithm for porous media reconstruction

    Lemmens, L., Rogiers, B., Jacques, D., Huysmans, M., Swennen, R., Urai, J. L., Desbois, G. & Laloy, E., 1 Nov 2019, In : Physical Review E. 100, 5, 053316.

    Research output: Contribution to journalArticle

  4. Emulation of CPU-demanding reactive transport models: a comparison of Gaussian processes, polynomial chaos expansion, and deep neural networks

    Laloy, E. & Jacques, D., 1 Oct 2019, In : Computational Geosciences. 23, 5, p. 1193-1215 23 p.

    Research output: Contribution to journalArticle

  5. A mesoscale framework for analysis of corrosion induced damage of concrete

    Seetharam, S., Laloy, E., Jivkov, A., Yu, L., Phung, Q. T., Phuong Pham, N., Kursten, B. & Druyts, F., 20 Aug 2019, In : Construction and Building Materials. 216, p. 347-361 14 p.

    Research output: Contribution to journalArticle

  6. Bayesian full-waveform tomography with application to crosshole ground penetrating radar data

    Hunziker, J., Laloy, E. & Linde, N., 1 Aug 2019, In : Geophysical Journal International. 218, 2, p. 913-931 19 p.

    Research output: Contribution to journalArticle

  7. Scale-dependent parameterization of groundwater–surface water interactions in a regional hydrogeological model

    Di Ciacca, A., Leterme, B., Laloy, E., Jacques, D. & Vanderborght, J., 27 Jun 2019, In : Journal of Hydrology. 576, p. 494-507 16 p.

    Research output: Contribution to journalArticle

  8. 2018
  9. HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

    Shen, C., Laloy, E., Elshorbagy, A., Albert, A., Bales, J., Chang, F-J., Ganguly, S., Hsu, K-L., Kifer, D., Fang, Z., Fang, K., Li, D., Li, X. & Tsai, W-P., 1 Nov 2018, In : Hydrology and Earth System Sciences. 22, p. 5639–5656 18 p.

    Research output: Contribution to journalArticle

  10. Training-Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network

    Laloy, E., Hérault, R., Jacques, D. & Linde, N., 8 Jan 2018, In : Water Resources Research. 54, 1, p. 381-406 26 p.

    Research output: Contribution to journalArticle

  11. 2017
  12. Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network

    Laloy, E., Hérault, R., Lee, J., Jacques, D. & Linde, N., 4 Oct 2017, In : Advances in Water Resources. 110, p. 387-405 18 p.

    Research output: Contribution to journalArticle

  13. Inference of multi-Gaussian relative permittivity fields by probabilistic inversion of crosshole ground-penetrating radar data

    Hunziker, J., Laloy, E. & Linde, N., 1 Sep 2017, In : GEOPHYSICS. 82, 5, p. 25-40 16 p.

    Research output: Contribution to journalArticle

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