{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T18:11:23Z","timestamp":1770142283819,"version":"3.49.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031948947","type":"print"},{"value":"9783031948954","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T00:00:00Z","timestamp":1756166400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T00:00:00Z","timestamp":1756166400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-031-94895-4_32","type":"book-chapter","created":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T07:24:30Z","timestamp":1756106670000},"page":"308-316","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Constraint-Aware Diffusion Models for\u00a0Trajectory Optimization"],"prefix":"10.1007","author":[{"given":"Anjian","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zihan","family":"Ding","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adji Bousso","family":"Dieng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryne","family":"Beeson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,26]]},"reference":[{"key":"32_CR1","unstructured":"Ajay, A., Du, Y., Gupta, A., Tenenbaum, J., Jaakkola, T., Agrawal, P.: Is conditional generative modeling all you need for decision-making? arXiv preprint arXiv:2211.15657 (2022)"},{"key":"32_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1017\/S0962492900002518","volume":"4","author":"PT Boggs","year":"1995","unstructured":"Boggs, P.T., Tolle, J.W.: Sequential quadratic programming. Acta Numerica 4, 1\u201351 (1995)","journal-title":"Acta Numerica"},{"key":"32_CR3","unstructured":"Botteghi, N., Califano, F., Poel, M., Brune, C.: Trajectory generation, control, and safety with denoising diffusion probabilistic models. arXiv preprint arXiv:2306.15512 (2023)"},{"key":"32_CR4","unstructured":"Chang, J., et al.: Denoising heat-inspired diffusion with insulators for collision free motion planning. arXiv preprint arXiv:2310.12609 (2023)"},{"key":"32_CR5","doi-asserted-by":"crossref","unstructured":"Chi, C., et al.: Diffusion policy: visuomotor policy learning via action diffusion. arXiv preprint arXiv:2303.04137 (2023)","DOI":"10.15607\/RSS.2023.XIX.026"},{"key":"32_CR6","first-page":"8780","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. Adv. Neural. Inf. Process. Syst. 34, 8780\u20138794 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"1","key":"32_CR7","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1137\/S0036144504446096","volume":"47","author":"PE Gill","year":"2005","unstructured":"Gill, P.E., Murray, W., Saunders, M.A.: SNOPT: an SQP algorithm for large-scale constrained optimization. SIAM Rev. 47(1), 99\u2013131 (2005)","journal-title":"SIAM Rev."},{"key":"32_CR8","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"32_CR9","unstructured":"Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)"},{"key":"32_CR10","first-page":"8633","volume":"35","author":"J Ho","year":"2022","unstructured":"Ho, J., Salimans, T., Gritsenko, A., Chan, W., Norouzi, M., Fleet, D.J.: Video diffusion models. Adv. Neural. Inf. Process. Syst. 35, 8633\u20138646 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"32_CR11","unstructured":"Hoogeboom, E., Satorras, V.G., Vignac, C., Welling, M.: Equivariant diffusion for molecule generation in 3D. In: International Conference on Machine Learning, pp. 8867\u20138887. PMLR (2022)"},{"key":"32_CR12","unstructured":"Janner, M., Du, Y., Tenenbaum, J.B., Levine, S.: Planning with diffusion for flexible behavior synthesis. arXiv preprint arXiv:2205.09991 (2022)"},{"key":"32_CR13","doi-asserted-by":"crossref","unstructured":"Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN 1995-International Conference on Neural Networks, vol.\u00a04, pp. 1942\u20131948. IEEE (1995)","DOI":"10.1109\/ICNN.1995.488968"},{"key":"32_CR14","unstructured":"Ketata, M.A., et al.: Diffdock-pp: rigid protein-protein docking with diffusion models. arXiv preprint arXiv:2304.03889 (2023)"},{"key":"32_CR15","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"32_CR16","unstructured":"Krishnamoorthy, S., Mashkaria, S.M., Grover, A.: Diffusion models for black-box optimization. arXiv preprint arXiv:2306.07180 (2023)"},{"issue":"4","key":"32_CR17","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1023\/A:1026500301312","volume":"18","author":"RH Leary","year":"2000","unstructured":"Leary, R.H.: Global optimization on funneling landscapes. J. Glob. Optim. 18(4), 367 (2000)","journal-title":"J. Glob. Optim."},{"key":"32_CR18","unstructured":"Li, A., Ding, Z., Dieng, A.B., Beeson, R.: Efficient and guaranteed-safe non-convex trajectory optimization with constrained diffusion model. arXiv preprint arXiv:2403.05571 (2024)"},{"key":"32_CR19","doi-asserted-by":"crossref","unstructured":"Maz\u00e9, F., Ahmed, F.: Diffusion models beat GANs on topology optimization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a037, pp. 9108\u20139116 (2023)","DOI":"10.1609\/aaai.v37i8.26093"},{"key":"32_CR20","unstructured":"Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press (1998)"},{"key":"32_CR21","unstructured":"Power, T., Soltani-Zarrin, R., Iba, S., Berenson, D.: Sampling constrained trajectories using composable diffusion models. In: IROS 2023 Workshop on Differentiable Probabilistic Robotics: Emerging Perspectives on Robot Learning (2023)"},{"key":"32_CR22","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"32_CR23","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"32_CR24","unstructured":"Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning, pp. 2256\u20132265. PMLR (2015)"},{"key":"32_CR25","unstructured":"Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020)"},{"key":"32_CR26","doi-asserted-by":"crossref","unstructured":"Sun, Z., Yang, Y.: Difusco: graph-based diffusion solvers for combinatorial optimization. In: Advances in Neural Information Processing Systems, vol. 36 (2024)","DOI":"10.1007\/978-1-4614-6624-6_97-1"},{"key":"32_CR27","unstructured":"Trabucco, B., Geng, X., Kumar, A., Levine, S.: Design-bench: benchmarks for data-driven offline model-based optimization. In: International Conference on Machine Learning, pp. 21658\u201321676. PMLR (2022)"},{"issue":"28","key":"32_CR28","doi-asserted-by":"publisher","first-page":"5111","DOI":"10.1021\/jp970984n","volume":"101","author":"DJ Wales","year":"1997","unstructured":"Wales, D.J., Doye, J.P.: Global optimization by basin-hopping and the lowest energy structures of Lennard-Jones clusters containing up to 110 atoms. J. Phys. Chem. A 101(28), 5111\u20135116 (1997)","journal-title":"J. Phys. Chem. A"},{"key":"32_CR29","unstructured":"Yang, Z., et al.: Compositional diffusion-based continuous constraint solvers. arXiv preprint arXiv:2309.00966 (2023)"}],"container-title":["Lecture Notes in Computer Science","Dynamic Data Driven Applications Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-94895-4_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,9]],"date-time":"2025-09-09T22:11:01Z","timestamp":1757455861000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-94895-4_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,26]]},"ISBN":["9783031948947","9783031948954"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-94895-4_32","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,26]]},"assertion":[{"value":"26 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DDDAS\/Infosymbiotics for Reliable AI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Dynamic Data Driven Applications Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Brunswick, NJ","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dddas2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dddas2024.rutgers.edu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}