UTOPYA为工业过程监控提供了首个融合物理信息与多模态数据的端到端方案，做异常检测和过程控制的工程师可以直接参考其架构设计，尤其适合数据稀缺场景下的部署决策。
UTOPYA是一个1520万参数的多模态框架，通过融合八种数据模态，解决了批处理过程中的异常检测难题。该框架采用特征线性调制（FiLM）条件跨模态注意力和门控融合，并引入物理信息正则化方案，强制时间平滑和热力学单调性。在119实验的多模态批处理蒸馏数据集上，UTOPYA在窗口级测试AUROC达到0.832，多信号实验级AUROC达到0.874，显著优于PCA、自编码器、孤立森林和LSTM自编码器等基线方法。消融实验表明，FiLM条件提供的静态上下文是关键因素，将实验级多信号AUROC从0.729提升至0.874。同时，研究揭示了实例归一化、Mixup、集成、测试时增强和随机权重平均等常用技术在此数据稀缺场景下反而会降低泛化性能。
UTOPYA: A Multimodal Deep Learning Framework for Physics-Informed Anomaly Detection and Time-Series Prediction
Anomaly detection in batch processes is hindered by transient dynamics, scarce fault labels, and reliance on single-modality sensor data. This work introduces UTOPYA (Unified Temporal Observation for Physics-Informed Anomaly Detection and Time-Series Prediction), a 15.2M-parameter multimodal framework that jointly addresses anomaly detection, time-series prediction, and phase classification in batch distillation by fusing eight data modalities through Feature-wise Linear Modulation (FiLM) conditioned cross-modal attention and gated fusion. A physics-informed regularisation scheme introduced in this work enforces temporal smoothness and thermodynamic monotonicity, while curriculum learning introduces training samples in order of physical difficulty. On the 119-experiment multimodal batch distillation dataset of Arweiler et al. (2026), UTOPYA achieves a window-level test AUROC of 0.832 and 0.874 under multi-signal experiment-level scoring, substantially outperforming four external baselines (PCA, autoencoder, Isolation Forest, and LSTM autoencoder) evaluated under identical conditions (+0.147 window-level AUROC over the best baseline). A multimodal ablation over 15~architectural configurations shows that static context via FiLM conditioning is the key enabler, lifting experiment-level multi-signal AUROC by +0.145 over the unimodal baseline (0.729 to 0.874). Separately, a training ablation across 14 design choices reveals that several widely-adopted techniques, including instance normalisation, Mixup, ensembling, test-time augmentation, and stochastic weight averaging, fail to improve or actively degrade generalisation in this data-scarce setting. These negative results expose a fundamental tension between smoothing-based regularisation and anomaly detection, providing practical guidance for multimodal process monitoring deployment.