eess.SY · 2026-07-15 · No. 54

Systems and Control, 2026-07-15.

3 new papers in eess.SY. Titles, authors, abstracts. Links to arXiv. Want this in your inbox every morning? Subscribe →

01 — The papers

3 entries
  1. 01

    Environment Parameter Gradient Theorem for Policy-Environment Co-Design in Reinforcement Learning

    Amber Srivastava

    eess.SY · cs.LG

    Reinforcement learning (RL) is traditionally concerned with learning a control policy for a fixed environment. In many engineering systems, however, the environment itself is alterable: physical or operational parameters can be tuned to shape the transition dynamics and costs experienced by the agent. This motivates jointly optimizing both the policy and the environment design parameters. To this end, we establish an Environment Parameter...

    arxiv.org/abs/2607.12590 · PDF

  2. 02

    Gradient-Free Topology Adaptation for Power Flow Surrogates via In-Context Whitening

    Ayushi Jolotia, Parikshit Pareek

    eess.SY · cs.LG

    Machine-learned surrogates for the AC power flow (ACPF) problem amortize the cost of repeated solves on a fixed network, but lose one to two orders of magnitude of accuracy when a line outage changes the topology. This degradation is an operator shift. The altered admittance matrix changes the input-to-output map, so identical inputs yield a different output distribution. Existing methods correct this with target-topology data and...

    arxiv.org/abs/2607.12241 · PDF

  3. 03

    Generating Physically Plausible Parachute Dynamics with Deep Generative Modeling

    Yulong Yang, Clara O'Farrell, Christine Allen-Blanchette

    eess.SY · cs.LG

    Accurately modeling the dynamics of planetary parachute and entry vehicle systems is critical for Entry, Descent, and Landing events such as vehicle separation and sensor activation. These dynamics are difficult to capture with traditional system-identification methods as parachute motion is highly nonlinear, the governing equations are not fully known, and relevant test data are scarce and expensive to acquire. In this work, we sidestep...

    arxiv.org/abs/2607.12143 · PDF

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