Wallace Mcaliley Arvind Renganathan Michael Steinbach Christopher Duffy Vipin Kumar Rahul Ghosh 2024 <div id="abstracts" data-extent="frontmatter"><div class="core-container"><div>Many environmental systems (e.g., hydrology basins) can be modeled as entity whose response (e.g., streamflow) depends on drivers (e.g., weather) conditioned on their characteristics (e.g., soil properties). We introduce Entity-aware Conditional Variational Inference (EA-CVI), a novel probabilistic inverse modeling approach, to deduce entity characteristics from observed driver-response data. EA-CVI infers probabilistic latent representations that can accurately predict response for diverse entities, particularly in out-of-sample few-shot settings. EA-CVI's latent embeddings encapsulate diverse entity characteristics within compact, low-dimensional representations. EA-CVI proficiently identifies dominant modes of variation in responses and offers the opportunity to infer a physical interpretation of the underlying attributes that shape these responses. EA-CVI can also generate new data samples by sampling from the learned distribution, making it useful in zero-shot scenarios. EA-CVI addresses the need for uncertainty estimation, particularly during extreme events, rendering it essential for data-driven decision-making in real-world applications. Extensive evaluations on a renowned hydrology benchmark dataset, CAMELS-GB, validate EA-CVI's abilities.</div></div></div> application/pdf 10.1137/1.9781611978032.38 en Society for Industrial and Applied Mathematics Towards entity-aware conditional variational inference for heterogeneous time-series prediction: An application to hydrology text