Monitoring moving bio-objects is currently of great interest for both fundamental and practical research. The advent of deep-learning algorithms has made it possible to automate the qualitative and quantitative analysis of the behavior of bio-objects recorded in video format. When processing such data, it is necessary to consider additional factors, such as background noise in the frame, the speed of the bio-object, and the need to reflect information about the previous (past) and subsequent (future) pose of the bio-object in one video frame. The preprocessed dataset must be suitable for verification by experts. This article proposes a method for preprocessing data to identify the behavior of a bio-object, a clear example of which is experiments on laboratory animals with the collection of video data. The method is based on combining information about a behavioral event presented in a sequence of frames with the addition of a native image and subsequent boundary detection using the Sobel filter. The resulting representation of a behavioral event is easily perceived by both human experts and neural networks of various architectures. The article presents the results of training several neural networks on the obtained dataset and proposes an effective neural network architecture (F1-score = 0.95) for identifying discrete events of biological objects’ behavior.

Fast full-field forecasting in petroleum reservoir simulation is important for field-development planning, well-control screening, uncertainty assessment, and production optimization, where many two-phase flow scenarios must be evaluated before final high-fidelity simulator verification. This study proposes a Coupled Multi-Physics Operator Learning (COMPOL)-based neural operator proxy with separate local–global Fourier neural operator branches for pressure and oil saturation, coupled through a Perceiver latent aggregation module. The model is trained on 2048 well configurations and evaluated on 512 disjoint held-out validation cases over a 20-step operational horizon generated with tNavigator. In this fixed-geology benchmark, the Coupled neural operator achieves the lowest error on three of the four reported physical metrics: well-level pressure error and both field- and well-level saturation errors. Transolver obtains the lowest full-field pressure error, indicating complementary strength on the smoother global pressure response. Qualitative maps show that the remaining errors are concentrated near wells and moving saturation fronts. A 30-case benchmark shows a $309\times$ speedup over tNavigator. These results suggest that field-specific operator branches with compact pressure–saturation coupling are a promising proxy strategy within the studied setting.

The observation of moving bio-object s is currently a topic of great interest in both fundamental and practical research. To illustrate, when developing and testing pharmaceuticals and vaccines, it is imperative to study their impact on the physiological state and behavior of an animal or human subject. The advent of deep learning algorithms has enabled the automation of qualitative and quantitative analysis of the behavior of bio-objects recorded in video format. It is essential that video data undergoes the appropriate preprocessing prior to training deep learning models. It is essential to consider additional factors, such as the background noise in the frame, the rapidity of bio-object movement, and the necessity to reflect information about the previous (past) and subsequent (future) pose of the bio-object in a single video frame. Moreover, the preprocessed dataset should be suitable for verification by its experts. This paper proposes a method of data preprocessing for the identification of bio-object behavior, with the laboratory animal experiments involving video data collection serving as an illustrative example. The method is based on the combination of information about a behavior event presented in a sequence of frames, with the addition of the native image and further boundary extraction by Sobel filter. Consequently, the behavior event representation obtained is readily comprehensible for both human experts and neural networks of varying architectures. The paper presents the outcomes of training multiple neural networks on the acquired data set and proposes an effective neural network architecture (accuracy = 0.95) for the identification of discrete behavior events of bio-objects.