Generative event sequence simulator with probability estimation

A processor-mediated method comprising:

• obtaining, with a computing system, a generative artificial intelligence (AI) model, the generative AI model having been trained to output a predicted sequence of events in response to input, wherein the predicted sequence of events are not natural language text tokens, and the events are part of an at least partially stochastic process that occurs at location in physical space;
• inputting, by the computing system, a first sequence of at least one event to the generative AI model, the at least one event being associated with a positional encoding representing a location in physical space at which the at least one event occurred or one or more event parameters;
• predicting, with the generative AI model, a second sequence of at least one event subsequent to the first sequence of events based on the first sequence of at least one event and the positional encoding or the one or more event parameters;
• and
• storing, with the computing system, the second sequence of at least one event in memory.

A processor-mediated method comprising:

• obtaining, with a computing system, a set of training data, the training data comprising a plurality of sequences of events, the sequences of events occurring in corresponding locations in physical space;
• tokenizing the locations in physical space of the events of the plurality of sequences of events as positional embeddings;
• training a generative artificial intelligence (AI) model to generate a subsequent sequence of events in response to input based on the training data and the tokenized locations of the events;
• and
• storing, with the computing system, the trained generative AI model in memory.