Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-06-30T14:27:13.064017Z
Paper Citation Record · LEDGER
Learning dynamical systems with biochemically informed neural ordinary differential equations
As of 18 August 2026, this Paper Citation Record lists 69 of 69 outbound references and 0 inbound Pith citation observations for arXiv:2605.24170.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
69 of 69 outbound references displayed
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- verified fuzzy67
- unresolved1
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 1729fab5-1409-431c-82ca-595f1c94c230 · outbound
This paper cites Die Kinetik der Invertinwirkung,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Die Kinetik der Invertinwirkung,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.839436Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 15c8d61f-a303-42b3-8826-29aed0441f49 · outbound
This paper cites Henri, Lois g´ en´ erales de l’action des diastases.
Learning dynamical systems with biochemically informed neural ordinary differential equations Henri, Lois g´ en´ erales de l’action des diastases
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.868278Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation a7ca96d2-253e-4009-98d0-8031cf3133d3 · outbound
This paper cites Th´ eorie g´ en´ erale de l’action de quelques diastases par Victor Henri [CR Acad. Sci. Paris 135 (1902) 916-919],.
Learning dynamical systems with biochemically informed neural ordinary differential equations Th´ eorie g´ en´ erale de l’action de quelques diastases par Victor Henri [CR Acad. Sci. Paris 135 (1902) 916-919],
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.820823Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 99daf087-64cb-4ec2-9367-b7e213bb8a52 · outbound
This paper cites One hundred years of michaelis–menten kinetics,.
Learning dynamical systems with biochemically informed neural ordinary differential equations One hundred years of michaelis–menten kinetics,
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.808449Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 380475cb-3790-4196-b4e3-f0adc1555b8b · outbound
This paper cites Studier over affiniteten,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Studier over affiniteten,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.810108Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation bd724479-0f56-4abe-8810-b83086c8ae1d · outbound
This paper cites 150 years of the mass action law,.
Learning dynamical systems with biochemically informed neural ordinary differential equations 150 years of the mass action law,
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.811551Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 2e137117-e373-4bac-bcf4-321732bc85b7 · outbound
This paper cites an unresolved cited work.
Learning dynamical systems with biochemically informed neural ordinary differential equations Unresolved cited work
Reference 7
- Resolution
- unresolved
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation e6e34186-af37-4df8-8ccd-fb7b4c80021b · outbound
This paper cites Volterra, Variazioni e fluttuazioni del numero d’individui in specie animali conviventi , vol.
Learning dynamical systems with biochemically informed neural ordinary differential equations Volterra, Variazioni e fluttuazioni del numero d’individui in specie animali conviventi , vol
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.794992Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 72898cbb-792d-48bb-837e-194e11932aad · outbound
This paper cites A contribution to the mathematical theory of epidemics,.
Learning dynamical systems with biochemically informed neural ordinary differential equations A contribution to the mathematical theory of epidemics,
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.798309Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 2369ff45-a3ba-4efa-a4ed-3496be163481 · outbound
This paper cites Uniformly accurate nonlinear transmission rate models arising from disease spread through pair contacts,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Uniformly accurate nonlinear transmission rate models arising from disease spread through pair contacts,
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.788983Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 50a17ebc-cf49-406f-8451-52bc83d23409 · outbound
This paper cites Biochemical systems analysis: I. Some mathematical properties of the rate law for the component enzymatic reactions,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Biochemical systems analysis: I. Some mathematical properties of the rate law for the component enzymatic reactions,
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.785666Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 077a96aa-4386-466b-93db-c2fcc07f5b6e · outbound
This paper cites Biochemical systems analysis: III. Dynamic solutions using a power-law approximation,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Biochemical systems analysis: III. Dynamic solutions using a power-law approximation,
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.782155Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 963d8054-27e9-4b99-b27e-7b2f21edf0ed · outbound
This paper cites Biochemical Systems Theory: A Review,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Biochemical Systems Theory: A Review,
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.784092Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 40c3c66d-1ea7-4276-9050-b988ea9206ca · outbound
This paper cites Dynamic simulation and metabolic re-design of a branched pathway using linlog kinetics,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Dynamic simulation and metabolic re-design of a branched pathway using linlog kinetics,
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.787380Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation e55fdf83-352b-4e6d-a821-82d6628e8c3c · outbound
This paper cites Myc dosage compensation is mediated by mirna-transcription factor interactions in aneuploid cancer,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Myc dosage compensation is mediated by mirna-transcription factor interactions in aneuploid cancer,
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.790998Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 7d107e96-78e1-48fc-b26d-33f34a4c61c7 · outbound
This paper cites Partition analysis and concept of net rate constants as tools in enzyme kinetics,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Partition analysis and concept of net rate constants as tools in enzyme kinetics,
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.813554Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation ef22e648-63b1-4682-8d47-9ab888839cd5 · outbound
This paper cites A schematic method of deriving the rate laws for enzyme-catalyzed reactions,.
Learning dynamical systems with biochemically informed neural ordinary differential equations A schematic method of deriving the rate laws for enzyme-catalyzed reactions,
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.780687Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation aa7a92c0-9e24-4014-9c1a-3e62dd87c48c · outbound
This paper cites A note on the kinetics of enzyme action,.
Learning dynamical systems with biochemically informed neural ordinary differential equations A note on the kinetics of enzyme action,
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.769151Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 61e664bb-15ad-4615-97bc-032c2f909d67 · outbound
This paper cites The possible effects of the aggregation of the molecules of hemoglobin on its dissociation curves,.
Learning dynamical systems with biochemically informed neural ordinary differential equations The possible effects of the aggregation of the molecules of hemoglobin on its dissociation curves,
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.773855Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation fe095153-1bd4-480a-a9f8-21f98a54e734 · outbound
This paper cites The Hill equation and the origin of quantitative pharmacology,.
Learning dynamical systems with biochemically informed neural ordinary differential equations The Hill equation and the origin of quantitative pharmacology,
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.778947Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 096571f3-8f83-4020-8592-fc4f9734b1be · outbound
This paper cites The control of flux,.
Learning dynamical systems with biochemically informed neural ordinary differential equations The control of flux,
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.767656Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b0670d56-9973-43de-b086-411f1309288d · outbound
This paper cites A linear steady-state treatment of enzymatic chains: general properties, control and effector strength,.
Learning dynamical systems with biochemically informed neural ordinary differential equations A linear steady-state treatment of enzymatic chains: general properties, control and effector strength,
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.775778Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 659cb3fb-3b98-4b6b-a9ed-17e9d8cb69f1 · outbound
This paper cites Bringing metabolic networks to life: convenience rate law and thermodynamic constraints,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Bringing metabolic networks to life: convenience rate law and thermodynamic constraints,
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.764413Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 63574595-358e-44a8-afeb-de14c1385ee3 · outbound
This paper cites Modular rate laws for enzymatic reactions: thermodynamics, elasticities and implementation,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Modular rate laws for enzymatic reactions: thermodynamics, elasticities and implementation,
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.762632Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation f518d5e1-0635-457a-9129-38b791eaf1f2 · outbound
This paper cites Cooperativity and saturation in biochemical networks: a saturable formalism using Taylor series approximations,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Cooperativity and saturation in biochemical networks: a saturable formalism using Taylor series approximations,
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.759165Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 22886e14-7689-473f-99c5-a5d3cb34f343 · outbound
This paper cites Comparison of unstructured kinetic bacterial growth models,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Comparison of unstructured kinetic bacterial growth models,
Reference 26
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.761077Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 0afb1652-90ba-413c-a37d-a132cb71bae0 · outbound
This paper cites Monod, Recherches sur la croissance des cultures bact´ eriennes.
Learning dynamical systems with biochemically informed neural ordinary differential equations Monod, Recherches sur la croissance des cultures bact´ eriennes
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.765923Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b220bb37-a824-4c49-a705-fef7b6abb0c3 · outbound
This paper cites La technique de culture continue, th {´ e} orie et applications,.
Learning dynamical systems with biochemically informed neural ordinary differential equations La technique de culture continue, th {´ e} orie et applications,
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.777367Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 8a099939-7152-4501-bc2e-88248f7cb2ca · outbound
This paper cites Haldane, Enzymes.
Learning dynamical systems with biochemically informed neural ordinary differential equations Haldane, Enzymes
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.822227Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation d35d3066-ff17-44e1-8fa4-37d074d0b2c6 · outbound
This paper cites A mathematical model for the continuous culture of microorganisms utilizing inhibitory substrates,.
Learning dynamical systems with biochemically informed neural ordinary differential equations A mathematical model for the continuous culture of microorganisms utilizing inhibitory substrates,
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.866701Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 6472d91c-02c9-4a4a-a1f4-fd5fa4143ee0 · outbound
This paper cites Moser, The dynamics of bacterial populations maintained in the chemostat.
Learning dynamical systems with biochemically informed neural ordinary differential equations Moser, The dynamics of bacterial populations maintained in the chemostat
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.870132Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 3fe4968a-1d0c-4a62-bbdf-63b9837951c7 · outbound
This paper cites The components of predation as revealed by a study of small mammal predation of the European pine sawfly,.
Learning dynamical systems with biochemically informed neural ordinary differential equations The components of predation as revealed by a study of small mammal predation of the European pine sawfly,
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.876658Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 1439b25f-70d2-4654-8784-625c9d089305 · outbound
This paper cites Some characteristics of simple types of predation and parasitism,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Some characteristics of simple types of predation and parasitism,
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.851989Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation d85e047f-9b39-4b07-8d72-32f099ad29ca · outbound
This paper cites A derivation of Holling’s type I, II and III functional responses in predator–prey systems,.
Learning dynamical systems with biochemically informed neural ordinary differential equations A derivation of Holling’s type I, II and III functional responses in predator–prey systems,
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.855644Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation fba8d480-b730-446c-9212-f72cf0311c5d · outbound
This paper cites Biologically informed NeuralODEs for genome-wide regulatory dynamics,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Biologically informed NeuralODEs for genome-wide regulatory dynamics,
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.848345Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b0dc4193-46f6-48ad-92a1-e778c1693104 · outbound
This paper cites Universal differential equations for systems biology: Current state and open problems,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Universal differential equations for systems biology: Current state and open problems,
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.837943Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 384961f5-6e8f-4685-90f6-19cbff635aea · outbound
This paper cites Physiology-informed regularisation enables training of universal differential equation systems for biological applications,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Physiology-informed regularisation enables training of universal differential equation systems for biological applications,
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.843149Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b1c7ddd2-9762-4981-be7c-1524f2da9a1e · outbound
This paper cites A hybrid neural ordinary differential equation model of the cardiovascular system,.
Learning dynamical systems with biochemically informed neural ordinary differential equations A hybrid neural ordinary differential equation model of the cardiovascular system,
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.841063Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 0fc5cf85-836a-4db1-8c74-06283588165b · outbound
This paper cites Modeling chemical reaction networks using neural ordinary differential equations,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Modeling chemical reaction networks using neural ordinary differential equations,
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.846739Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 3641fd0f-351d-4ced-9184-9f9197d55228 · outbound
This paper cites Control of dynamical systems with neural networks,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Control of dynamical systems with neural networks,
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.850391Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 1db45d56-4927-4ad0-87ce-45acf3972214 · outbound
This paper cites Learning dynamical systems with side information,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Learning dynamical systems with side information,
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.832473Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 3780489b-56b2-47c7-923c-e0d0f9a4502e · outbound
This paper cites Learning dynamical systems with side information,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Learning dynamical systems with side information,
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.835891Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 58ceeb28-09da-4a14-9a3b-0c76e9e717b8 · outbound
This paper cites Optimal control of agent-based models via surrogate modeling,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Optimal control of agent-based models via surrogate modeling,
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.831059Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation d9fc5054-a7a4-477d-8992-a033c22cb8e2 · outbound
This paper cites Interpretable polynomial neural ordinary differential equations,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Interpretable polynomial neural ordinary differential equations,
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.834400Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation bc01be0a-3b3d-465f-8b24-e202c6441294 · outbound
This paper cites Control of medical digital twins with artificial neural networks,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Control of medical digital twins with artificial neural networks,
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.827647Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation f2205668-959f-408b-8116-b1537d00f2cc · outbound
This paper cites Learning effective stochastic differential equations from microscopic simulations: Linking stochastic numerics to deep learning,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Learning effective stochastic differential equations from microscopic simulations: Linking stochastic numerics to deep learning,
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.825674Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 4159c328-c953-4ac0-931c-d525eb74613b · outbound
This paper cites Reconstructing noisy gene regulation dynamics using extrinsic-noise-driven neural stochastic differential equations,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Reconstructing noisy gene regulation dynamics using extrinsic-noise-driven neural stochastic differential equations,
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.824070Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 81ad512a-9774-4397-b594-90854263f8d8 · outbound
This paper cites AI-Aristotle: A physics-informed framework for systems biology gray-box identification,.
Learning dynamical systems with biochemically informed neural ordinary differential equations AI-Aristotle: A physics-informed framework for systems biology gray-box identification,
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.829337Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 23cbceb1-d8f7-42b2-af67-dda3c5057038 · outbound
This paper cites Why RELU units sometimes die: Analysis of single-unit error backpropagation in neural networks,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Why RELU units sometimes die: Analysis of single-unit error backpropagation in neural networks,
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.853943Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 4e2c7930-f887-4775-b03b-863d4c438b7f · outbound
This paper cites Multilayer feedforward networks are universal approximators.
Learning dynamical systems with biochemically informed neural ordinary differential equations Multilayer feedforward networks are universal approximators
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.863143Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 4045c2c4-d98b-4295-9c29-d71def90a573 · outbound
This paper cites The expressive power of neural networks: A view from the width,.
Learning dynamical systems with biochemically informed neural ordinary differential equations The expressive power of neural networks: A view from the width,
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.803483Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation dc76a86d-4286-4afd-ba57-140c7eefdf62 · outbound
This paper cites Benefits of depth in neural networks,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Benefits of depth in neural networks,
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.800163Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 799f0662-dd9b-42c6-9d9a-65fdeb1ffa9d · outbound
This paper cites Biochemical systems analysis: II. The steady-state solutions for an n-pool system using a power-law approximation,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Biochemical systems analysis: II. The steady-state solutions for an n-pool system using a power-law approximation,
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.801819Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation b7b986e7-697e-4447-8048-68dfb2a306b4 · outbound
This paper cites Input convex neural networks,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Input convex neural networks,
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.804875Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 98821425-52b2-4f5d-81ec-2ec5365fc3df · outbound
This paper cites PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data,.
Learning dynamical systems with biochemically informed neural ordinary differential equations PySINDy: A Python package for the sparse identification of nonlinear dynamical systems from data,
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.806924Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation eadc1cf6-adb7-4ff4-b0c0-8abe15fb936e · outbound
This paper cites PySINDy: A comprehensive python package for robust sparse system identification,.
Learning dynamical systems with biochemically informed neural ordinary differential equations PySINDy: A comprehensive python package for robust sparse system identification,
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.817372Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 26386126-2d41-40ba-9bde-f2392c7546fd · outbound
This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems.
Learning dynamical systems with biochemically informed neural ordinary differential equations Discovering governing equations from data by sparse identification of nonlinear dynamical systems
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.793473Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 30984175-e748-47bf-85bf-7411ba2f61df · outbound
This paper cites PySINDy.
Learning dynamical systems with biochemically informed neural ordinary differential equations PySINDy
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.818829Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c467120d-b67d-42ab-9359-afcec79b872c · outbound
This paper cites Extreme theory of functional connections: A fast physics-informed neural network method for solving ordinary and partial differential equations.
Learning dynamical systems with biochemically informed neural ordinary differential equations Extreme theory of functional connections: A fast physics-informed neural network method for solving ordinary and partial differential equations
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.772326Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 2d35edd4-b113-49d4-a4cc-1836019d21db · outbound
This paper cites Physics-informed neural networks and functional interpolation for stiff chemical kinetics,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Physics-informed neural networks and functional interpolation for stiff chemical kinetics,
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.815167Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 48f7ad16-cbea-4082-8195-e2eea3b8d78d · outbound
This paper cites Symbolic Regression is NP-hard.
Learning dynamical systems with biochemically informed neural ordinary differential equations Symbolic Regression is NP-hard
Reference 61
- Resolution
- verified exact
- arxiv_id, observed 2026-06-30T14:34:45.696206Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation f5e8a31e-8841-4a83-a6d8-ea7abc4e4c1a · outbound
This paper cites gplearn: Genetic programming in python with a scikit-learn inspired and compatible api,.
Learning dynamical systems with biochemically informed neural ordinary differential equations gplearn: Genetic programming in python with a scikit-learn inspired and compatible api,
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.873260Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 5268369c-f8d7-474c-bb8d-d25a79411dbc · outbound
This paper cites Barnes and G.
Learning dynamical systems with biochemically informed neural ordinary differential equations Barnes and G
Reference 63
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.858806Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 41fab32a-9e55-4a12-9fbf-762aa3b28fda · outbound
This paper cites Computer model for mechanisms underlying ultradian oscillations of insulin and glucose,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Computer model for mechanisms underlying ultradian oscillations of insulin and glucose,
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.860750Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 07135631-68bf-4e35-8600-1c9eba63e9a7 · outbound
This paper cites Complex coordination of multi-scale cellular responses to environmental stress,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Complex coordination of multi-scale cellular responses to environmental stress,
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.864734Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 6ee05fee-a655-482a-8cdd-c542e4166ed7 · outbound
This paper cites The origins of enzyme kinetics,.
Learning dynamical systems with biochemically informed neural ordinary differential equations The origins of enzyme kinetics,
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.871691Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation c9ed6e93-5d87-4917-a092-96bd8b5be52e · outbound
This paper cites Enzymes longmans,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Enzymes longmans,
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.874822Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation d0136ef1-badc-4533-a1b4-afb360834273 · outbound
This paper cites The reversible Hill equation: how to incorporate cooperative enzymes into metabolic models,.
Learning dynamical systems with biochemically informed neural ordinary differential equations The reversible Hill equation: how to incorporate cooperative enzymes into metabolic models,
Reference 68
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.857339Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 65b898b2-b034-4435-8d77-517829a94013 · outbound
This paper cites Biodegradation kinetics of benzene, toluene, and phenol as single and mixed substrates for Pseudomonas putida F1,.
Learning dynamical systems with biochemically informed neural ordinary differential equations Biodegradation kinetics of benzene, toluene, and phenol as single and mixed substrates for Pseudomonas putida F1,
Reference 69
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-07-08T23:05:44.844968Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Pith citing papers
No inbound Pith citation observations are available.