7 papers
Deterministic Decomposition of Stochastic Generative Dynamics
Xingyu Song, Yuan Mei, Naoya Takeishi
Modern generative models can be understood as probability transport from a simple base distribution to a target data distribution. Deterministic transport models offer tractable ve…
M: Reframing Training Measures for Discretized Physical Simulations
Yuan Mei, Xingyu Song, Xiaowen Song +1
Neural surrogate models for physical simulations are trained on discretized samples of continuous domains, where the induced empirical measure leads to uneven supervision, biasing…
Accurate Open-Loop Control of a Soft Continuum Robot Through Visually Learned Latent Representations
Henrik Krauss, Johann Licher, Naoya Takeishi +2
This work addresses open-loop control of a soft continuum robot (SCR) from video-learned latent dynamics. Visual Oscillator Networks (VONs) from previous work are used, that provid…
Sharpness-Aware Hybrid Model Learning for Architecture-Agnostic Parameter Estimation
Naoya Takeishi
Hybrid modeling, the combination of machine learning models and scientific mathematical models, enables flexible and robust data-driven prediction with partial interpretability. Ho…
Simulation-Efficient Cosmological Inference with Multi-Fidelity SBI
Leander Thiele, Adrian E. Bayer, Naoya Takeishi
The simulation cost for cosmological simulation-based inference can be decreased by combining simulation sets of varying fidelity. We propose an approach to such multi-fidelity inf…
Kolmogorov-Smirnov GAN
Maciej Falkiewicz, Naoya Takeishi, Alexandros Kalousis
We propose a novel deep generative model, the Kolmogorov-Smirnov Generative Adversarial Network (KSGAN). Unlike existing approaches, KSGAN formulates the learning process as a mini…