UQLab is an open source, general purpose uncertainty quantification framework (Marelli and Sudret, 2014, https://www.uqlab.com) that has recently turned 10 since its first public beta release in July 2015. With a user base of over 10,000 unique users worldwide, it is widely considered a reference tool in the uncertainty quantification community. It includes multiple computational modules that provide easy access to both beginners and experts to state-of-the-art tools, such as surrogate modeling, advanced statistical inference, reliability and sensitivity analysis, and much more, and is actively maintained and developed at ETH Zurich. In this talk, we showcase how it can be efficiently used to perform computational intensive analyses on complex time-variant systems, such as, e.g., reliability analysis and Bayesian model calibration, at highly reduced computational costs. We will focus on its recently introduced native support for handling time-series data and models, and in particular on its state-of-the-art data-driven time-variant surrogate modeling module, which includes polynomial-chaos NARX (PC-NARX) (Mai et al., 2016), as well as the recently developed functional- and manifold- NARX (Schär et al., 2025, 2026). References Mai, C. V., Spiridonakos, M. D., Chatzi, E.N. and Sudret, B., Surrogate modeling for stochastic dynamical systems by combining nonlinear autoregressive with exogenous input models and polynomial chaos expansions. International Journal for Uncertainty Quantification, 6(4), 2016. Marelli, S. and Sudret, B., UQLab: A framework for uncertainty quantification in Matlab. In Vulnerability, uncertainty, and risk: quantification, mitigation, and management (ICVRAM2014), p. 2554-2563, 2014. Schär, S., Marelli, S. and Sudret, B., Surrogate modeling with functional nonlinear autoregressive models (F-NARX). Reliability Engineering & System Safety, p.111276, 2025. Schär, S., Marelli, S. and Sudret, B., mNARX+: A surrogate model for complex dynamical systems using manifold-NARX and automatic feature selection. Computer Methods in Applied Mechanics and Engineering, 449, p.118550, 2026.