Niklas Linde

14 papers Journal 13Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Diffusion models for multivariate subsurface generation and efficient probabilistic inversion. Comput. Geosci. Roberto Miele, Niklas Linde
2025	J	`jnl`	An Energy-Based Model Approach to Rare Event Probability Estimation. SIAM/ASA J. Uncertain. Quantification Lea Friedli, David Ginsbourger, Arnaud Doucet, Niklas Linde
2025	J	`jnl`	Diffusion models for multivariate subsurface generation and efficient probabilistic inversion. CoRR Roberto Miele, Niklas Linde
2024	J	`jnl`	Conditioning of multiple-point statistics simulations to indirect geophysical data. Comput. Geosci. Shiran Levy, Lea Friedli, Grégoire Mariéthoz, Niklas Linde
2024	J	`jnl`	Deep generative networks for multivariate fullstack seismic data inversion using inverse autoregressive flows. Comput. Geosci. Roberto Miele, Shiran Levy, Niklas Linde, Amilcar Soares, Leonardo Azevedo
2023	J	`jnl`	Efficient Bayesian travel-time tomography with geologically-complex priors using sensitivity-informed polynomial chaos expansion and deep generative networks. CoRR Giovanni Angelo Meles, Macarena Amaya, Shiran Levy, Stefano Marelli, Niklas Linde
2023	J	`jnl`	Uncertainty Quantification and Experimental Design for Large-Scale Linear Inverse Problems under Gaussian Process Priors. SIAM/ASA J. Uncertain. Quantification Cédric Travelletti, David Ginsbourger, Niklas Linde
2023	J	`jnl`	Variational Bayesian inference with complex geostatistical priors using inverse autoregressive flows. Comput. Geosci. Shiran Levy, Eric Laloy, Niklas Linde
2021	—	`conf`	Fast ABC with Joint Generative Modelling and Subset Simulation. LOD Eliane Maalouf, David Ginsbourger, Niklas Linde
2021	J	`jnl`	Fast ABC with joint generative modelling and subset simulation. CoRR Eliane Maalouf, David Ginsbourger, Niklas Linde
2021	J	`jnl`	Uncertainty Quantification and Experimental Design for large-scale linear Inverse Problems under Gaussian Process Priors. CoRR Cédric Travelletti, David Ginsbourger, Niklas Linde
2019	J	`jnl`	Gradient-based deterministic inversion of geophysical data with generative adversarial networks: Is it feasible? Comput. Geosci. Eric Laloy, Niklas Linde, Cyprien Ruffino, Romain Hérault, Gilles Gasso, Diederik Jacques
2017	J	`jnl`	Efficient training-image based geostatistical simulation and inversion using a spatial generative adversarial neural network. CoRR Eric Laloy, Romain Hérault, Diederik Jacques, Niklas Linde
2015	J	`jnl`	Feature-preserving interpolation and filtering of environmental time series. Environ. Model. Softw. Grégoire Mariéthoz, Niklas Linde, Damien Jougnot, Hassan Rezaee